Search results for: shooting accuracy

Authors: Belyihun Muchie

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This study investigates the low English oral communication skills of 5 second-year English major students at Debark University. It aims to identify the key factors contributing to their weaknesses and propose effective interventions to improve their spoken English proficiency. Mixed-methods research will be employed, utilizing observations, questionnaires, and semi-structured interviews to gather data from the participants. To clearly identify these factors, structured and informal observations will be employed; the former will be used to identify their fluency, pronunciation, vocabulary use, and grammar accuracy, and the later will be suited to observe the natural interactions and communication patterns of learners in the classroom setting. The questionnaires will assess their self-perceptions of their skills, perceived barriers to fluency, and preferred learning styles. Interviews will also delve deeper into their experiences and explore specific obstacles faced in oral communication. Data analysis will involve both quantitative and qualitative responses. The structured observation and questionnaire will be analyzed quantitatively, whereas the informal observation and interview transcripts will be analyzed thematically. Findings will be used to identify the major causes of low oral communication skills, such as limited vocabulary, grammatical errors, pronunciation difficulties, or lack of confidence. They are also helpful to develop targeted solutions addressing these causes, such as intensive pronunciation practice, conversation simulations, personalized feedback, or anxiety-reduction techniques. Finally, the findings will guide designing an intervention plan for implementation during the action research phase. The study's outcomes are expected to provide valuable insights into the challenges faced by English major students in developing oral communication skills, contribute to the development of evidence-based interventions for improving spoken English proficiency in similar contexts, and offer practical recommendations for English language instructors and curriculum developers to enhance student learning outcomes. By addressing the specific needs of these students and implementing tailored interventions, this research aims to bridge the gap between theoretical knowledge and practical speaking ability, equipping them with the confidence and skills to flourish in English communication settings.

Keywords: oral communication skills, mixed-methods, evidence-based interventions, spoken English proficiency

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Authors: Justo Rodriguez, Daming Chen, Amador M. Guzman

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The detection, treatment, and control of rapidly propagating, deadly viruses such as COVID-19, require the development of inexpensive, fast, and accurate devices to address the urgent needs of the population. Microfluidics-based sensors are amongst the different methods and techniques for detection that are easy to use. A micro analyzer is defined as a microfluidics-based sensor, composed of a network of microchannels with varying functions. Given their size, portability, and accuracy, they are proving to be more effective and convenient than other solutions. A micro analyzer based on the concept of “Lab on a Chip” presents advantages concerning other non-micro devices due to its smaller size, and it is having a better ratio between useful area and volume. The integration of multiple processes in a single microdevice reduces both the number of necessary samples and the analysis time, leading the next generation of analyzers for the health-sciences. In some applications, the flow of solution within the microchannels is originated by a pressure gradient, which can produce adverse effects on biological samples. A more efficient and less dangerous way of controlling the flow in a microchannel-based analyzer is applying an electric field to induce the fluid motion and either enhance or suppress the mixing process. Electrokinetic flows are characterized by no less than two non-dimensional parameters: the electric Rayleigh number and its geometrical aspect ratio. In this research, stable and unstable flows have been studied numerically (and when possible, will be experimental) in a T-shaped microchannel. Additionally, unstable electrokinetic flows for Rayleigh numbers higher than critical have been characterized. The flow mixing enhancement was quantified in relation to the stretching and folding that fluid particles undergo when they are subjected to supercritical electrokinetic flows. Computational simulations were carried out using a finite element-based program while working with the flow mixing concepts developed by Gollub and collaborators. Hundreds of seeded massless particles were tracked along the microchannel from the entrance to exit for both stable and unstable flows. After post-processing, their trajectories, the folding and stretching values for the different flows were found. Numerical results show that for supercritical electrokinetic flows, the enhancement effects of the folding and stretching processes become more apparent. Consequently, there is an improvement in the mixing process, ultimately leading to a more homogenous mixture.

Keywords: microchannel, stretching and folding, electro kinetic flow mixing, micro-analyzer

Procedia PDF Downloads 102

Authors: Umut Yıldız, Berkin Kurtuluş, Yunus Emre Muslubaş

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Thrust vectoring, especially in military aviation, is a concept that sees much use to improve maneuverability in already agile aircraft. As this concept is fairly new and cost intensive to design and test, computational methods are useful in easing the preliminary design process. Computational Fluid Dynamics (CFD) can be utilized in many forms to simulate nozzle flow, and there exist various CFD studies in both 2D mechanical and 3D injection based thrust vectoring, and yet, 3D mechanical thrust vectoring analyses, at this point in time, are lacking variety. Additionally, the freely available test data is constrained to limited pitch angles and geometries. In this study, based on a test case provided by NASA, both steady and unsteady 3D CFD simulations are conducted to examine the aerodynamic performance of a mechanical thrust vectoring nozzle model and to validate the utilized numerical model. Steady analyses are performed to verify the flow characteristics of the nozzle at pitch angles of 0, 10 and 20 degrees, and the results are compared with experimental data. It is observed that the pressure data obtained on the inner surface of the nozzle at each specified pitch angle and under different flow conditions with pressure ratios of 1.5, 2 and 4, as well as at azimuthal angle of 0, 45, 90, 135, and 180 degrees exhibited a high level of agreement with the corresponding experimental results. To validate the CFD model, the insights from the steady analyses are utilized, followed by unsteady analyses covering a wide range of pitch angles from 0 to 20 degrees. Throughout the simulations, a mesh morphing method using a carefully calculated mathematical shape deformation model that simulates the vectored nozzle shape exactly at each point of its travel is employed to dynamically alter the divergent part of the nozzle over time within this pitch angle range. The mesh morphing based vectored nozzle shapes were compared with the drawings provided by NASA, ensuring a complete match was achieved. This computational approach allowed for the creation of a comprehensive database of results without the need to generate separate solution domains. The database contains results at every 0.01° increment of nozzle pitch angle. The unsteady analyses, generated using the morphing method, are found to be in excellent agreement with experimental data, further confirming the accuracy of the CFD model.

Keywords: thrust vectoring, computational fluid dynamics, 3d mesh morphing, mathematical shape deformation model

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Authors: D. F. Carvalho, A. O. Uscamayta, J. C. Guerrero, H. F. Oliveira, P. M. Azevedo-Marques

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The creation of vector contours slices (ROIs) on body silhouettes in oncologic patients is an important step during the radiotherapy planning in clinic and hospitals to ensure the accuracy of oncologic treatment. The radiotherapy planning of patients is performed by complex softwares focused on analysis of tumor regions, protection of organs at risk (OARs) and calculation of radiation doses for anomalies (tumors). These softwares are supplied for a few manufacturers and run over sophisticated workstations with vector processing presenting a cost of approximately twenty thousand dollars. The Brazilian project SIPRAD (Radiotherapy Planning System) presents a proposal adapted to the emerging countries reality that generally does not have the monetary conditions to acquire some radiotherapy planning workstations, resulting in waiting queues for new patients treatment. The SIPRAD project is composed by a set of integrated and interoperabilities softwares that are able to execute all stages of radiotherapy planning on simple personal computers (PCs) in replace to the workstations. The goal of this work is to present an image processing technique, computationally feasible, that is able to perform an automatic contour delineation in patient body silhouettes (SIPRAD-Body). The SIPRAD-Body technique is performed in tomography slices under grayscale images, extending their use with a greedy algorithm in three dimensions. SIPRAD-Body creates an irregular polyhedron with the Canny Edge adapted algorithm without the use of preprocessing filters, as contrast and brightness. In addition, comparing the technique SIPRAD-Body with existing current solutions is reached a contours similarity at least 78%. For this comparison is used four criteria: contour area, contour length, difference between the mass centers and Jaccard index technique. SIPRAD-Body was tested in a set of oncologic exams provided by the Clinical Hospital of the University of Sao Paulo (HCRP-USP). The exams were applied in patients with different conditions of ethnology, ages, tumor severities and body regions. Even in case of services that have already workstations, it is possible to have SIPRAD working together PCs because of the interoperability of communication between both systems through the DICOM protocol that provides an increase of workflow. Therefore, the conclusion is that SIPRAD-Body technique is feasible because of its degree of similarity in both new radiotherapy planning services and existing services.

Keywords: radiotherapy, image processing, DICOM RT, Treatment Planning System (TPS)

Procedia PDF Downloads 271

Authors: Abulbasit G. Abdulsayid, Zinab F. Abdulla, Asma A. Omer

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On the ground that the orifice plate is relatively inexpensive, requires very little maintenance and only calibrated during the occasion of plant turnaround, the orifice plate has turned to be in a real prevalent use in gas industry. Inaccuracy of measurement in the fiscal metering stations may highly be accounted to be the most vital factor for mischarges in the natural gas industry in Libya. A very trivial error in measurement can add up a fast escalating financial burden to the custodian transactions. The unaccounted gas quantity transferred annually via orifice plates in Libya, could be estimated in an extent of multi-million dollars. As the oil and gas wealth is the solely source of income to Libya, every effort is now being exerted to improve the accuracy of existing orifice metering facilities. Discharge coefficient has become pivotal in current researches undertaken in this regard. Hence, increasing the knowledge of the flow field in a typical orifice meter is indispensable. Recently and in a drastic pace, the CFD has become the most time and cost efficient versatile tool for in-depth analysis of fluid mechanics, heat and mass transfer of various industrial applications. Getting deeper into the physical phenomena lied beneath and predicting all relevant parameters and variables with high spatial and temporal resolution have been the greatest weighing pros counting for CFD. In this paper, flow phenomena for air passing through an orifice meter were numerically analyzed with CFD code based modeling, giving important information about the effect of orifice plate specifications on the discharge coefficient for three different tappings locations, i.e., flange tappings, D and D/2 tappings compared with vena contracta tappings. Discharge coefficients were paralleled with discharge coefficients estimated by ISO 5167. The influences of orifice plate bore thickness, orifice plate thickness, beveled angle, perpendicularity and buckling of the orifice plate, were all duly investigated. A case of an orifice meter whose pipe diameter of 2 in, beta ratio of 0.5 and Reynolds number of 91100, was taken as a model. The results highlighted that the discharge coefficients were highly responsive to the variation of plate specifications and under all cases, the discharge coefficients for D and D/2 tappings were very close to that of vena contracta tappings which were believed as an ideal arrangement. Also, in general sense, it was appreciated that the standard equation in ISO 5167, by which the discharge coefficient was calculated, cannot capture the variation of the plate specifications and thus further thorough considerations would be still needed.

Keywords: CFD, discharge coefficients, orifice meter, orifice plate specifications

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Authors: Niyi Awofeso, Yunes Gaber, Moyosola Bamidele

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Since most social media platforms are accessible anytime and anywhere where Internet connections and smartphones are available, the invisibility of the reader raises questions about accuracy, appropriateness and comprehensibility of social media communication. Furthermore, the identity and motives of individuals and organizations who post articles on social media sites are not always transparent. In the health sector, through socially networked platforms constitute a common source of health-related information, given their purported wealth of information. Nevertheless, fake blogs and sponsored postings for marketing 'natural cures' pervade most commonly used social media platforms, thus complicating readers’ abilities to access and understand trustworthy health-related information. This purposive sampling study of 120 participants aged 18-35 year in UAE was conducted between September and December 2017, and explored commonly used social media platforms, frequency of use of social media for accessing health related information, and approaches for assessing the trustworthiness of health information on social media platforms. Results indicate that WhatsApp (95%), Instagram (87%) and Youtube (82%) were the most commonly used social media platforms among respondents. Majority of respondents (81%) indicated that they regularly access social media to get health-associated information. More than half of respondents (55%) with non-chronic health status relied on unsolicited messages to obtain health-related information. Doctors’ health blogs (21%) and social media sites of international healthcare organizations (20%) constitute the most trusted source of health information among respondents, with UAE government health agencies’ social media accounts trusted by 15% of respondents. Cardiovascular diseases, diabetes, and hypertension were the most commonly searched topics on social media (29%), followed by nutrition (20%) and skin care (16%). Majority of respondents (41%) rely on reliability of hits on Google search engines, 22% check for health information only from 'reliable' social media sites, while 8% utilize 'logic' to ascertain reliability of health information. As social media has rapidly become an integral part of the health landscape, it is important that health care policy makers, healthcare providers and social media companies collaborate to promote the positive aspects of social media for young people, whilst mitigating the potential negatives. Utilizing popular social media platforms for posting reader-friendly health information will achieve high coverage. Improving youth digital literacy will facilitate easier access to trustworthy information on the internet.

Keywords: social media, United Arab Emirates, youth engagement, digital literacy

Procedia PDF Downloads 99

Authors: Leigh Ann Wilson, Melanie Borrego

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The purpose of institutional learning outcomes (ILOs) is to assess what students across the university know and what they do not. The issue is gathering this information in a systematic and usable way. This presentation will explain how one institution has engineered this process for both student success and maximum faculty curriculum and course design input. At Brandman University, there are three levels of learning outcomes: course, program, and institutional. Institutional Learning Outcomes (ILOs) are mapped to specific courses. Faculty course developers write the signature assignments (SAs) in alignment with the Institutional Learning Outcomes for each course. These SAs use a specific rubric that is applied consistently by every section and every instructor. Each year, the 12-member General Education Team (GET), as a part of their work, conducts the calibration and assessment of the university-wide SAs and the related rubrics for one or two of the five ILOs. GET members, who are senior faculty and administrators who represent each of the university's schools, lead the calibration meetings. Specifically, calibration is a process designed to ensure the accuracy and reliability of evaluating signature assignments by working with peer faculty to interpret rubrics and compare scoring. These calibration meetings include the full time and adjunct faculty members who teach the course to ensure consensus on the application of the rubric. Each calibration session is chaired by a GET representative as well as the course custodian/contact where the ILO signature assignment resides. The overall calibration process GET follows includes multiple steps, such as: contacting and inviting relevant faculty members to participate; organizing and hosting calibration sessions; and reviewing and discussing at least 10 samples of student work from class sections during the previous academic year, for each applicable signature assignment. Conversely, the commitment for calibration teams consist of attending two virtual meetings lasting up to three hours in duration. The first meeting focuses on interpreting the rubric, and the second meeting involves comparing scores for sample work and sharing feedback about the rubric and assignment. Next, participants are expected to follow all directions provided and participate actively, and respond to scheduling requests and other emails within 72 hours. The virtual meetings are recorded for future institutional use. Adjunct faculty are paid a small stipend after participating in both calibration meetings. Full time faculty can use this work on their annual faculty report for "internal service" credit.

Keywords: assessment, assurance of learning, course design, institutional learning outcomes, rubrics, signature assignments

Procedia PDF Downloads 258

Authors: Tomasz Łęga, Marta Sosnowska, Mirosława Panasiuk, Lilit Hovhannisyan, Beata Gromadzka, Marcin Olszewski, Sabina Zoledowska, Dawid Nidzworski

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Toxic heavy metal ion contamination of industrial wastewater has recently become a significant environmental concern in many regions of the world. Although the majority of heavy metals are naturally occurring elements found on the earth's surface, anthropogenic activities such as mining and smelting, industrial production, and agricultural use of metals and metal-containing compounds are responsible for the majority of environmental contamination and human exposure. The permissible limits (ppm) for heavy metals in food, water and soil are frequently exceeded and considered hazardous to humans, other organisms, and the environment as a whole. Human exposure to highly nickel-polluted environments causes a variety of pathologic effects. In 2008, nickel received the shameful name of “Allergen of the Year” (GILLETTE 2008). According to the dermatologist, the frequency of nickel allergy is still growing, and it can’t be explained only by fashionable piercing and nickel devices used in medicine (like coronary stents and endoprostheses). Effective remediation methods for removing heavy metal ions from soil and water are becoming increasingly important. Among others, methods such as chemical precipitation, micro- and nanofiltration, membrane separation, conventional coagulation, electrodialysis, ion exchange, reverse and forward osmosis, photocatalysis and polymer or carbon nanocomposite absorbents have all been investigated so far. The importance of environmentally sustainable industrial production processes and the conservation of dwindling natural resources has highlighted the need for affordable, innovative biosorptive materials capable of recovering specific chemical elements from dilute aqueous solutions. The use of combinatorial phage display techniques for selecting and recognizing material-binding peptides with a selective affinity for any target, particularly inorganic materials, has gained considerable interest in the development of advanced bio- or nano-materials. However, due to the limitations of phage display libraries and the biopanning process, the accuracy of molecular recognition for inorganic materials remains a challenge. This study presents the isolation, identification and characterisation of metal binding phage clones that preferentially recover nickel.

Keywords: Heavy metal recovery, cleaning water, phage display, nickel

Procedia PDF Downloads 73

Authors: Hamed Kalhori, Lin Ye

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In this study, an inverse method was developed to reconstruct the magnitude and duration of impact forces exerted to a rectangular carbon fibre-epoxy composite honeycomb sandwich panel. The dynamic signals captured by Piezoelectric (PZT) sensors installed on the panel remotely from the impact locations were utilized to reconstruct the impact force generated by an instrumented hammer through an extended deconvolution approach. Two discretized forms of convolution integral are considered; the traditional one with an explicit transfer function and the modified one without an explicit transfer function. Deconvolution, usually applied to reconstruct the time history (e.g. magnitude) of a stochastic force at a defined location, is extended to identify both the location and magnitude of the impact force among a number of potential impact locations. It is assumed that a number of impact forces are simultaneously exerted to all potential locations, but the magnitude of all forces except one is zero, implicating that the impact occurs only at one location. The extended deconvolution is then applied to determine the magnitude as well as location (among the potential ones), incorporating the linear superposition of responses resulted from impact at each potential location. The problem can be categorized into under-determined (the number of sensors is less than that of impact locations), even-determined (the number of sensors equals that of impact locations), or over-determined (the number of sensors is greater than that of impact locations) cases. For an under-determined case, it comprises three potential impact locations and one PZT sensor for the rectangular carbon fibre-epoxy composite honeycomb sandwich panel. Assessments are conducted to evaluate the factors affecting the precision of the reconstructed force. Truncated Singular Value Decomposition (TSVD) and the Tikhonov regularization are independently chosen to regularize the problem to find the most suitable method for this system. The selection of optimal value of the regularization parameter is investigated through L-curve and Generalized Cross Validation (GCV) methods. In addition, the effect of different width of signal windows on the reconstructed force is examined. It is observed that the impact force generated by the instrumented impact hammer is sensitive to the impact locations of the structure, having a shape from a simple half-sine to a complicated one. The accuracy of the reconstructed impact force is evaluated using the correlation co-efficient between the reconstructed force and the actual one. Based on this criterion, it is concluded that the forces reconstructed by using the extended deconvolution without an explicit transfer function together with Tikhonov regularization match well with the actual forces in terms of magnitude and duration.

Keywords: honeycomb composite panel, deconvolution, impact localization, force reconstruction

Procedia PDF Downloads 516

Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

Procedia PDF Downloads 471

Authors: Lin Zhao, Hanqiao Jiang, Junjian Li

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Polymers injected into elevated-temperature reservoirs inevitably suffer from thermal degradation, resulting in severe viscosity loss and poor flooding performance. However, for polymer flooding in such reservoirs, present simulators fail to provide accurate results for lack of description on thermal degradation. In light of this, the objectives of this paper are to provide a simulation model for polymer flooding with thermal degradation and study the effect of thermal degradation on polymer flooding in elevated-temperature reservoirs. Firstly, a thermal degradation experiment was conducted to obtain the degradation law of polymer concentration and viscosity. Different types of polymers degraded in the Thermo tank with elevated temperatures. Afterward, based on the obtained law, a streamline-assistant model was proposed to simulate the degradation process under in-situ flow conditions. Model validation was performed with field data from a well group of an offshore oilfield. Finally, the effect of thermal degradation on polymer flooding was studied using the proposed model. Experimental results showed that the polymer concentration remained unchanged, while the viscosity degraded exponentially with time after degradation. The polymer viscosity was functionally dependent on the polymer degradation time (PDT), which represented the elapsed time started from the polymer particle injection. Tracing the real flow path of polymer particle was required. Therefore, the presented simulation model was streamline-assistant. Equation of PDT vs. time of flight (TOF) along streamline was built by the law of polymer particle transport. Based on the field polymer sample and dynamic data, the new model proved its accuracy. Study of degradation effect on polymer flooding indicated: (1) the viscosity loss increased with TOF exponentially in the main body of polymer-slug and remained constant in the slug front; (2) the responding time of polymer flooding was delayed, but the effective time was prolonged; (3) the breakthrough of subsequent water was eased; (4) the capacity of polymer adjusting injection profile was diminished; (5) the incremental recovery was reduced significantly. In general, the effect of thermal degradation on polymer flooding performance was rather negative. This paper provides a more comprehensive insight into polymer thermal degradation in both the physical process and field application. The proposed simulation model offers an effective means for simulating the polymer flooding process with thermal degradation. The negative effect of thermal degradation suggests that the polymer thermal stability should be given full consideration when designing polymer flooding project in elevated-temperature reservoirs.

Keywords: polymer flooding, elevated-temperature reservoir, thermal degradation, numerical simulation

Procedia PDF Downloads 115

Authors: C. Brecher, M. Fey, F. Du Bois-Reymond, S. Neus

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Additive manufacturing has emerged into a fast-growing section within the manufacturing technologies. Established machine tool manufacturers, such as DMG MORI, recently presented machine tools combining milling and laser welding. By this, machine tools can realize a higher degree of flexibility and a shorter production time. Still there are challenges that have to be accounted for in terms of maintaining the necessary machining accuracy - especially due to thermal effects arising through the use of high power laser processing units. To study the thermal behavior of laser-integrated machine tools, it is essential to analyze and simulate the thermal behavior of machine components, individual and assembled. This information will help to design a geometrically stable machine tool under the influence of high power laser processes. This paper presents an approach to decrease the loss of machining precision due to thermal impacts. Real effects of laser machining processes are considered and thus enable an optimized design of the machine tool, respective its components, in the early design phase. Core element of this approach is a matched FEM model considering all relevant variables arising, e.g. laser power, angle of laser beam, reflective coefficients and heat transfer coefficient. Hence, a systematic approach to obtain this matched FEM model is essential. Indicating the thermal behavior of structural components as well as predicting the laser beam path, to determine the relevant beam intensity on the structural components, there are the two constituent aspects of the method. To match the model both aspects of the method have to be combined and verified empirically. In this context, an essential machine component of a five axis machine tool, the turn-swivel table, serves as the demonstration object for the verification process. Therefore, a turn-swivel table test bench as well as an experimental set-up to measure the beam propagation were developed and are described in the paper. In addition to the empirical investigation, a simulative approach of the described types of experimental examination is presented. Concluding, it is shown that the method and a good understanding of the two core aspects, the thermo-elastic machine behavior and the laser beam path, as well as their combination helps designers to minimize the loss of precision in the early stages of the design phase.

Keywords: additive manufacturing, laser beam machining, machine tool, thermal effects

Procedia PDF Downloads 244

Authors: Ahmed S. Fayed, Umima M. Mansour

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Three polyvinyl chloride membrane sensors were developed for the electrochemical evaluation of ferrous, manganese and zinc ions. The sensors were used for assaying metal ions in cervical mucus (CM) of Egyptian river buffalo-cows (Bubalus bubalis) as their levels vary dependent on cyclical hormone variation during different phases of estrus cycle. The presented sensors are based on using ionophores, β-cyclodextrin (β-CD), hydroxypropyl β-cyclodextrin (HP-β-CD) and sulfocalix-4-arene (SCAL) for sensors 1, 2 and 3 for Fe²⁺, Mn²⁺ and Zn²⁺, respectively. Dioctyl phthalate (DOP) was used as the plasticizer in a polymeric matrix of polyvinylchloride (PVC). For increasing the selectivity and sensitivity of the sensors, each sensor was enriched with a suitable complexing agent, which enhanced the sensor’s response. For sensor 1, β-CD was mixed with bathophenanthroline; for sensor 2, porphyrin was incorporated with HP-β-CD; while for sensor 3, oxine was the used complexing agent with SCAL. Linear responses of 10^-7-10^-2 M with cationic slopes of 53.46, 45.01 and 50.96 over pH range 4-8 were obtained using coated graphite sensors for ferrous, manganese and zinc ionic solutions, respectively. The three sensors were validated, according to the IUPAC guidelines. The obtained results by the presented potentiometric procedures were statistically analyzed and compared with those obtained by atomic absorption spectrophotometric method (AAS). No significant differences for either accuracy or precision were observed between the two techniques. Successful application for the determination of the three studied cations in CM, for the purpose to determine the proper time for artificial insemination (AI) was achieved. The results were compared with those obtained upon analyzing the samples by AAS. Proper detection of estrus and correct time of AI was necessary to maximize the production of buffaloes. In this experiment, 30 multi-parous buffalo-cows were in second to third lactation and weighting 415-530 kg, and were synchronized with OVSynch protocol. Samples were taken in three times around ovulation, on day 8 of OVSynch protocol, on day 9 (20 h before AI) and on day 10 (1 h before AI). Beside analysis of trace elements (Fe²⁺, Mn²⁺ and Zn²⁺) in CM using the three sensors, the samples were analyzed for the three cations and also Cu²⁺ by AAS in the CM samples and blood samples. The results obtained were correlated with hormonal analysis of serum samples and ultrasonography for the purpose of determining of the optimum time of AI. The results showed significant differences and powerful correlation with Zn²⁺ composition of CM during heat phase and the ovulation time, indicating that the parameter could be used as a tool to decide optimal time of AI in buffalo-cows.

Keywords: PVC Sensors, buffalo-cows, cyclodextrins, atomic absorption spectrophotometry, artificial insemination, OVSynch protocol

Procedia PDF Downloads 198

Authors: L. Serrano-Lara, J.M Alba-Gómez

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Qubbet el–Hawa Project has been documented archaeological artifacts as 3d models by laser scanning technique since 2015. Currently, research has obtained the right methodology to develop a high accuracy photographic texture for each geometrical 3D model. Furthermore, the right methodology to attach the complete digital surrogate into a 3DPDF document has been obtained; it is used as a catalogue worksheet that brings archaeological data and, at the same time, allows us to obtain precise measurements, volume calculations and cross-section mapping of each scanned artifact. This validated archaeological documentation is the first step for dissemination, application as Qubbet el-Hawa Virtual Museum, and, moreover, multi-sensory experience through 3D print archaeological artifacts. Material culture from four funerary complexes constructed in West Aswan has become physical replicas opening the archaeological research process itself and offering creative possibilities on museology or educational projects. This paper shares a method of acquiring texture for scanning´s output product in order to achieve a 3DPDF archaeological cataloguing, and, on the other hand, to allow the colorfully 3D printing of singular archaeological artifacts. The proposed method has undergone two concrete cases, a polychrome wooden ushabti, and, a cartonnage mask belonging to a lady, bought recovered on intact tomb QH34aa. Both 3D model results have been implemented on three main applications, archaeological 3D catalogue, public dissemination activities, and the 3D artifact model in a bachelor education program. Due to those three already mentioned applications, productive interaction among spectator and three-dimensional artifact have been increased; moreover, functionality as archaeological documentation has been consolidated. Finding the right methodology to assign a specific color to each vector on the geometric 3D model, we had been achieved two essential archaeological applications. Firstly, 3DPDF as a display document for an archaeological catalogue, secondly, the possibility to obtain a colored 3d printed object to be displayed in public exhibitions. Obsolescences 3D models have become updated archaeological documentation of QH43aa tomb cultural material. Therefore, Qubbet el-Hawa Project has been actualized the educational potential of its results thanks to a multi-sensory experience that arose from 3d scanned´s archaeological artifacts.

Keywords: 3D printed, 3D scanner, Middle Kingdom, Qubbet el-Hawa necropolis, virtual archaeology

Procedia PDF Downloads 116

Authors: Mohamad Molavi Nojumi, Xiaodong Wang

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In this research fracture behaviour of linear elastic isotropic functionally graded materials (FGMs) are investigated using modified finite element method (FEM). FGMs are advantageous because they enhance the bonding strength of two incompatible materials, and reduce the residual stress and thermal stress. Ceramic/metals are a main type of FGMs. Ceramic materials are brittle. So, there is high possibility of crack existence during fabrication or in-service loading. In addition, damage analysis is necessary for a safe and efficient design. FEM is a strong numerical tool for analyzing complicated problems. Thus, FEM is used to investigate the fracture behaviour of FGMs. Here an accurate 9-node biquadratic quadrilateral graded element is proposed in which the influence of the variation of material properties is considered at the element level. The stiffness matrix of graded elements is obtained using the principle of minimum potential energy. The implementation of graded elements prevents the forced sudden jump of material properties in traditional finite elements for modelling FGMs. Numerical results are verified with existing solutions. Different numerical simulations are carried out to model stationary crack problems in nonhomogeneous plates. In these simulations, material variation is supposed to happen in directions perpendicular and parallel to the crack line. Two special linear and exponential functions have been utilized to model the material gradient as they are mostly discussed in literature. Also, various sizes of the crack length are considered. A major difference in the fracture behaviour of FGMs and homogeneous materials is related to the break of material symmetry. For example, when the material gradation direction is normal to the crack line, even under applying the mode I loading there exists coupled modes I and II of fracture which originates from the induced shear in the model. Therefore, the necessity of the proper modelling of the material variation should be considered in capturing the fracture behaviour of FGMs specially, when the material gradient index is high. Fracture properties such as mode I and mode II stress intensity factors (SIFs), energy release rates, and field variables near the crack tip are investigated and compared with results obtained using conventional homogeneous elements. It is revealed that graded elements provide higher accuracy with less effort in comparison with conventional homogeneous elements.

Keywords: finite element, fracture mechanics, functionally graded materials, graded element

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

Procedia PDF Downloads 201

Authors: Priscila A. Bernardes, Marcos E. Buzanskas, Luciana C. A. Regitano, Ricardo V. Ventura, Danisio P. Munari

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Genotype imputation has been used to reduce genomic selections costs. In order to increase haplotype detection accuracy in methods that considers the linkage disequilibrium, another approach could be used, such as combined genotype data from different panels. Therefore, this study aimed to evaluate the linkage disequilibrium and haplotype blocks in two high-density panels before and after the imputation to a combined panel in Nelore beef cattle. A total of 814 animals were genotyped with the Illumina BovineHD BeadChip (IHD), wherein 93 animals (23 bulls and 70 progenies) were also genotyped with the Affymetrix Axion Genome-Wide BOS 1 Array Plate (AHD). After the quality control, 809 IHD animals (509,107 SNPs) and 93 AHD (427,875 SNPs) remained for analyses. The combined genotype panel (CP) was constructed by merging both panels after quality control, resulting in 880,336 SNPs. Imputation analysis was conducted using software FImpute v.2.2b. The reference (CP) and target (IHD) populations consisted of 23 bulls and 786 animals, respectively. The linkage disequilibrium and haplotype blocks studies were carried out for IHD, AHD, and imputed CP. Two linkage disequilibrium measures were considered; the correlation coefficient between alleles from two loci (r²) and the |D’|. Both measures were calculated using the software PLINK. The haplotypes' blocks were estimated using the software Haploview. The r² measurement presented different decay when compared to |D’|, wherein AHD and IHD had almost the same decay. For r², even with possible overestimation by the sample size for AHD (93 animals), the IHD presented higher values when compared to AHD for shorter distances, but with the increase of distance, both panels presented similar values. The r² measurement is influenced by the minor allele frequency of the pair of SNPs, which can cause the observed difference comparing the r² decay and |D’| decay. As a sum of the combinations between Illumina and Affymetrix panels, the CP presented a decay equivalent to a mean of these combinations. The estimated haplotype blocks detected for IHD, AHD, and CP were 84,529, 63,967, and 140,336, respectively. The IHD were composed by haplotype blocks with mean of 137.70 ± 219.05kb, the AHD with mean of 102.10kb ± 155.47, and the CP with mean of 107.10kb ± 169.14. The majority of the haplotype blocks of these three panels were composed by less than 10 SNPs, with only 3,882 (IHD), 193 (AHD) and 8,462 (CP) haplotype blocks composed by 10 SNPs or more. There was an increase in the number of chromosomes covered with long haplotypes when CP was used as well as an increase in haplotype coverage for short chromosomes (23-29), which can contribute for studies that explore haplotype blocks. In general, using CP could be an alternative to increase density and number of haplotype blocks, increasing the probability to obtain a marker close to a quantitative trait loci of interest.

Keywords: Bos taurus indicus, decay, genotype imputation, single nucleotide polymorphism

Procedia PDF Downloads 255

Authors: M. A. Okezue, K. L. Clase, S. R. Byrn

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The requirement for maintaining data integrity in laboratory operations is critical for regulatory compliance. Automation of procedures reduces incidence of human errors. Quality control laboratories located in low-income economies may face some barriers in attempts to automate their processes. Since data from quality control tests on pharmaceutical products are used in making regulatory decisions, it is important that laboratory reports are accurate and reliable. Zinc Sulphate (ZnSO₄) tablets is used in treatment of diarrhea in pediatric population, and as an adjunct therapy for COVID-19 regimen. Unfortunately, zinc content in these formulations is determined titrimetrically; a manual analytical procedure. The assay for ZnSO4 tablets involves time-consuming steps that contain mathematical formulae prone to calculation errors. To achieve consistency, save costs, and improve data integrity, validated spreadsheets were developed to simplify the two critical steps in the analysis of ZnSO4 tablets: standardization of 0.1M Sodium Edetate (EDTA) solution, and the complexometric titration assay procedure. The assay method in the United States Pharmacopoeia was used to create a process flow for ZnSO₄ tablets. For each step in the process, different formulae were input into two spreadsheets to automate calculations. Further checks were created within the automated system to ensure validity of replicate analysis in titrimetric procedures. Validations were conducted using five data sets of manually computed assay results. The acceptance criteria set for the protocol were met. Significant p-values (p < 0.05, α = 0.05, at 95% Confidence Interval) were obtained from students’ t-test evaluation of the mean values for manual-calculated and spreadsheet results at all levels of the analysis flow. Right-first-time analysis and principles of data integrity were enhanced by use of the validated spreadsheet calculators in titrimetric evaluations of ZnSO₄ tablets. Human errors were minimized in calculations when procedures were automated in quality control laboratories. The assay procedure for the formulation was achieved in a time-efficient manner with greater level of accuracy. This project is expected to promote cost savings for laboratory business models.

Keywords: data integrity, spreadsheets, titrimetry, validation, zinc sulphate tablets

Procedia PDF Downloads 151

Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

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The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

Procedia PDF Downloads 111

Authors: Mohamed Dakki, Genevieve Robin, Marie Suet, Abdeljebbar Qninba, Mohamed A. El Agbani, Asmâa Ouassou, Rhimou El Hamoumi, Hichem Azafzaf, Sami Rebah, Claudia Feltrup-Azafzaf, Nafouel Hamouda, Wed a.L. Ibrahim, Hosni H. Asran, Amr A. Elhady, Haitham Ibrahim, Khaled Etayeb, Essam Bouras, Almokhtar Saied, Ashrof Glidan, Bakar M. Habib, Mohamed S. Sayoud, Nadjiba Bendjedda, Laura Dami, Clemence Deschamps, Elie Gaget, Jean-Yves Mondain-Monval, Pierre Defos Du Rau

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In biodiversity monitoring, large datasets are becoming more and more widely available and are increasingly used globally to estimate species trends and con- servation status. These large-scale datasets challenge existing statistical analysis methods, many of which are not adapted to their size, incompleteness and heterogeneity. The development of scalable methods to impute missing data in incomplete large-scale monitoring datasets is crucial to balance sampling in time or space and thus better inform conservation policies. We developed a new method based on penalized Poisson models to impute and analyse incomplete monitoring data in a large-scale framework. The method al- lows parameterization of (a) space and time factors, (b) the main effects of predic- tor covariates, as well as (c) space–time interactions. It also benefits from robust statistical and computational capability in large-scale settings. The method was tested extensively on both simulated and real-life waterbird data, with the findings revealing that it outperforms six existing methods in terms of missing data imputation errors. Applying the method to 16 waterbird species, we estimated their long-term trends for the first time at the entire North African scale, a region where monitoring data suffer from many gaps in space and time series. This new approach opens promising perspectives to increase the accuracy of species-abundance trend estimations. We made it freely available in the r package ‘lori’ (https://CRAN.R-project.org/package=lori) and recommend its use for large- scale count data, particularly in citizen science monitoring programmes.

Keywords: biodiversity monitoring, high-dimensional statistics, incomplete count data, missing data imputation, waterbird trends in North-Africa

Procedia PDF Downloads 124

Authors: Zsolt János Viharos, Krisztián Balázs Kis, Imre Paniti, Gábor Belső, Péter Németh, János Farkas

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The paper presents the results and industrial applications in the production setup period estimation based on industrial data inherited from the field of polymer cutting. The literature of polymer cutting is very limited considering the number of publications. The first polymer cutting machine is known since the second half of the 20th century; however, the production of polymer parts with this kind of technology is still a challenging research topic. The products of the applying industrial partner must met high technical requirements, as they are used in medical, measurement instrumentation and painting industry branches. Typically, 20% of these parts are new work, which means every five years almost the entire product portfolio is replaced in their low series manufacturing environment. Consequently, it requires a flexible production system, where the estimation of the frequent setup periods' lengths is one of the key success factors. In the investigation, several (input) parameters have been studied and grouped to create an adequate training information set for an artificial neural network as a base for the estimation of the individual setup periods. In the first group, product information is collected such as the product name and number of items. The second group contains material data like material type and colour. In the third group, surface quality and tolerance information are collected including the finest surface and tightest (or narrowest) tolerance. The fourth group contains the setup data like machine type and work shift. One source of these parameters is the Manufacturing Execution System (MES) but some data were also collected from Computer Aided Design (CAD) drawings. The number of the applied tools is one of the key factors on which the industrial partners’ estimations were based previously. The artificial neural network model was trained on several thousands of real industrial data. The mean estimation accuracy of the setup periods' lengths was improved by 30%, and in the same time the deviation of the prognosis was also improved by 50%. Furthermore, an investigation on the mentioned parameter groups considering the manufacturing order was also researched. The paper also highlights the manufacturing introduction experiences and further improvements of the proposed methods, both on the shop floor and on the quotation preparation fields. Every week more than 100 real industrial setup events are given and the related data are collected.

Keywords: artificial neural network, low series manufacturing, polymer cutting, setup period estimation

Procedia PDF Downloads 222

Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

Procedia PDF Downloads 252

Authors: Samuel Faboyede, Obiamaka Nwobu, Samuel Fakile, Dickson Mukoro

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In the present dynamic business environment of corporate governance and regulations, financial reporting is an inevitable and extremely significant process for every business enterprise. Several financial elements such as Annual Reports, Quarterly Reports, ad-hoc filing, and other statutory/regulatory reports provide vital information to the investors and regulators, and establish trust and rapport between the internal and external stakeholders of an organization. Investors today are very demanding, and emphasize greatly on authenticity, accuracy, and reliability of financial data. For many companies, the Internet plays a key role in communicating business information, internally to management and externally to stakeholders. Despite high prominence being attached to external reporting, it is disconnected in most companies, who generate their external financial documents manually, resulting in high degree of errors and prolonged cycle times. Chief Executive Officers and Chief Financial Officers are increasingly susceptible to endorsing error-laden reports, late filing of reports, and non-compliance with regulatory acts. There is a lack of common platform to manage the sensitive information – internally and externally – in financial reports. The Internet financial reporting language known as eXtensible Business Reporting Language (XBRL) continues to develop in the face of challenges and has now reached the point where much of its promised benefits are available. This paper looks at the emergence of this revolutionary twenty-first century language of digital reporting. It posits that today, the world is on the brink of an Internet revolution that will redefine the ‘business reporting’ paradigm. The new Internet technology, eXtensible Business Reporting Language (XBRL), is already being deployed and used across the world. It finds that XBRL is an eXtensible Markup Language (XML) based information format that places self-describing tags around discrete pieces of business information. Once tags are assigned, it is possible to extract only desired information, rather than having to download or print an entire document. XBRL is platform-independent and it will work on any current or recent-year operating system, or any computer and interface with virtually any software. The paper concludes that corporate stakeholders and the government cannot afford to ignore the XBRL. It therefore recommends that all must act locally and think globally now via the adoption of XBRL that is changing the face of worldwide business reporting.

Keywords: XBRL, financial reporting, internet, internal and external reports

Procedia PDF Downloads 257

Authors: Deniz A. Apostol, Denyl A. Apostol, Oliver T. Macapinlac, George S. Katigbak

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Ang lupa ay buhay at ang buhay ay lupa (land is life and life is land). Based on the 2015 census, the Indigenous Peoples (IPs) population in the Philippines is estimated to be 11.3-20.2 million. They hail from various regions, possess distinct cultures, but encounter shared struggles in territorial disputes. Itogon, the largest Benguet municipality, is home to the Ibaloi, Kankanaey, and other Igorot tribes. Despite having three (3) Ancestral Domains (ADs), Itogon is predominantly labeled as timberland or forest. These overlapping land classifications highlight the presence of inconsistencies in national laws and jurisdictions. This study aims to analyze surveying procedures used by the Igorots, NCIP, and DENR in mapping the Itogon AD Boundaries, show land boundary delineation conflicts, propose surveying guidelines, and recommend 3D Participatory Mapping as geomatics solution for updated AD reference maps. Interpretative Phenomenological Analysis (IPA), Comparative Legal Analysis (CLA), and Map Overlay Analysis (MOA) were utilized to examine the interviews, compare land policies and surveying procedures, and identify differences and overlaps in conflicting land boundaries. In the IPA, master themes identified were AD Definition (rights, responsibilities, restrictions), AD Overlaps (land classifications, political boundaries, ancestral domains, land laws/policies), and Other Conflicts (with other agencies, misinterpretations, suggestions), as considerations for mapping ADs. CLA focused on conflicting surveying procedures: AD Definitions, Surveying Equipment, Surveying Methods, Map Projections, Order of Accuracy, Monuments, Survey Parties, Pre-survey, Survey Proper, and Post-survey procedures. MOA emphasized the land area percentage of conflicting areas, showcasing the impact of misaligned surveying procedures. The findings are summarized through a Land Administration Domain Model (LADM) Conflict, for AD versus AD and Political Boundaries. The products of this study are identification of land conflict factors, survey guidelines recommendations, and contested land area computations. These can serve as references for revising survey manuals, updating AD Sustainable Development and Protection Plans, and making amendments to laws.

Keywords: ancestral domain, gis, indigenous people, land policies, participatory mapping, surveying, survey procedures

Procedia PDF Downloads 59

Authors: Hesam Gholami

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Elevated suspended sediment loads in riverine systems resulting from accelerated erosion due to human activities are a serious threat to the sustainable management of watersheds and ecosystem services therein worldwide. Therefore, mitigation of deleterious sediment effects as a distributed or non-point pollution source in the catchments requires reliable provenance information. Sediment tracing or sediment fingerprinting, as a combined process consisting of sampling, laboratory measurements, different statistical tests, and the application of mixing or unmixing models, is a useful technique for discriminating the sources of sediments. From 1996 to the present, different aspects of this technique, such as grouping the sources (spatial and individual sources), discriminating the potential sources by different statistical techniques, and modification of mixing and unmixing models, have been introduced and modified by many researchers worldwide, and have been applied to identify the provenance of fine materials in agricultural, rural, mountainous, and coastal catchments, and in large catchments with numerous lakes and reservoirs. In the last two decades, efforts exploring the uncertainties associated with sediment fingerprinting results have attracted increasing attention. The frameworks used to quantify the uncertainty associated with fingerprinting estimates can be divided into three groups comprising Monte Carlo simulation, Bayesian approaches and generalized likelihood uncertainty estimation (GLUE). Given the above background, the primary goal of this study was to apply geochemical fingerprinting within the GLUE framework in the estimation of sub-basin spatial sediment source contributions in the arid Mehran River catchment in southern Iran, which drains into the Persian Gulf. The accuracy of GLUE predictions generated using four different sets of statistical tests for discriminating three sub-basin spatial sources was evaluated using 10 virtual sediments (VS) samples with known source contributions using the root mean square error (RMSE) and mean absolute error (MAE). Based on the results, the contributions modeled by GLUE for the western, central and eastern sub-basins are 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%), respectively. According to the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), our suggested modeling approach is an accurate technique to quantify the source of sediments in the catchments. Overall, the estimated source proportions can help watershed engineers plan the targeting of conservation programs for soil and water resources.

Keywords: sediment source tracing, generalized likelihood uncertainty estimation, virtual sediment mixtures, Iran

Procedia PDF Downloads 48

Authors: E. Menga, S. Hernandez

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Finite Element Models (FEMs) are widely used in order to study and predict the dynamic properties of structures and usually, the prediction can be obtained with much more accuracy in the case of a single component than in the case of assemblies. Especially for structural dynamics studies, in the low and middle frequency range, most complex FEMs can be seen as assemblies made by linear components joined together at interfaces. From a modelling and computational point of view, these types of joints can be seen as localized sources of stiffness and damping and can be modelled as lumped spring/damper elements, most of time, characterized by nonlinear constitutive laws. On the other side, most of FE programs are able to run nonlinear analysis in time-domain. They treat the whole structure as nonlinear, even if there is one nonlinear degree of freedom (DOF) out of thousands of linear ones, making the analysis unnecessarily expensive from a computational point of view. In this work, a methodology in order to obtain the nonlinear frequency response of structures, whose nonlinearities can be considered as localized sources, is presented. The work extends the well-known Structural Dynamic Modification Method (SDMM) to a nonlinear set of modifications, and allows getting the Nonlinear Frequency Response Functions (NLFRFs), through an ‘updating’ process of the Linear Frequency Response Functions (LFRFs). A brief summary of the analytical concepts is given, starting from the linear formulation and understanding what the implications of the nonlinear one, are. The response of the system is formulated in both: time and frequency domain. First the Modal Database is extracted and the linear response is calculated. Secondly the nonlinear response is obtained thru the NL SDMM, by updating the underlying linear behavior of the system. The methodology, implemented in MATLAB, has been successfully applied to estimate the nonlinear frequency response of two systems. The first one is a two DOFs spring-mass-damper system, and the second example takes into account a full aircraft FE Model. In spite of the different levels of complexity, both examples show the reliability and effectiveness of the method. The results highlight a feasible and robust procedure, which allows a quick estimation of the effect of localized nonlinearities on the dynamic behavior. The method is particularly powerful when most of the FE Model can be considered as acting linearly and the nonlinear behavior is restricted to few degrees of freedom. The procedure is very attractive from a computational point of view because the FEM needs to be run just once, which allows faster nonlinear sensitivity analysis and easier implementation of optimization procedures for the calibration of nonlinear models.

Keywords: frequency response, nonlinear dynamics, structural dynamic modification, softening effect, rubber

Procedia PDF Downloads 246

Authors: Serpil Kilic, Ihsan Burak Cam, Murat Kilic, Timur Tongur

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Interest in the lead (Pb) has considerably increased due to knowledge about the potential toxic effects of this element, recently. Exposure to heavy metals above the acceptable limit affects human health. Indeed, Pb is accumulated through food chains up to toxic concentrations; therefore, it can pose an adverse potential threat to human health. A sensitive and reliable method for determination of Pb element in red pepper were improved in the present study. Samples (33 red pepper products having different brands) were purchased from different markets in Turkey. The selected method validation criteria (linearity, Limit of Detection, Limit of Quantification, recovery, and trueness) demonstrated. Recovery values close to 100% showed adequate precision and accuracy for analysis. According to the results of red pepper analysis, all of the tested lead element in the samples was determined at various concentrations. A Perkin- Elmer ELAN DRC-e model ICP-MS system was used for detection of Pb. Organic red pepper was used to obtain a matrix for all method validation studies. The certified reference material, Fapas chili powder, was digested and analyzed, together with the different sample batches. Three replicates from each sample were digested and analyzed. The results of the exposure levels of the elements were discussed considering the scientific opinions of the European Food Safety Authority (EFSA), which is the European Union’s (EU) risk assessment source associated with food safety. The Target Hazard Quotient (THQ) was described by the United States Environmental Protection Agency (USEPA) for the calculation of potential health risks associated with long-term exposure to chemical pollutants. THQ value contains intake of elements, exposure frequency and duration, body weight and the oral reference dose (RfD). If the THQ value is lower than one, it means that the exposed population is assumed to be safe and 1 < THQ < 5 means that the exposed population is in a level of concern interval. In this study, the THQ of Pb was obtained as < 1. The results of THQ calculations showed that the values were below one for all the tested, meaning the samples did not pose a health risk to the local population. This work was supported by The Scientific Research Projects Coordination Unit of Akdeniz University. Project Number: FBA-2017-2494.

Keywords: lead analyses, red pepper, risk assessment, daily exposure

Procedia PDF Downloads 143

Authors: Mingxia Zhu, Matthew Tingchi Liu

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AI grows gradually in the market with its efficiency and accuracy, influencing people’s perceptions, attitude, and even consequential behaviors. Current study extends prior research by focusing on AI’s impact on consumers’ morality. First, study 1 tested individuals’ believes about AI and human’s moral perceptions and people’s attribution of moral worth to AI and human. Moral perception refers to a computational system an entity maintains to detect and identify moral violations, while moral worth here denotes whether individual regard an entity as worthy of moral treatment. To identify the effect of AI on consumers’ morality, two studies were employed. Study 1 is a within-subjects survey, while study 2 is an experimental study. In the study 1, one hundred and forty participants were recruited through online survey company in China (M_age = 27.31 years, SD = 7.12 years; 65% female). The participants were asked to assign moral perception and moral worth to AI and human. A paired samples t-test reveals that people generally regard that human has higher moral perception (M_Human = 6.03, SD = .86) than AI (M_AI = 2.79, SD = 1.19; t(139) = 27.07, p < .001; Cohen’s d = 1.41). In addition, another paired samples t-test results showed that people attributed higher moral worth to the human personnel (M_Human = 6.39, SD = .56) compared with AIs (M_AI = 5.43, SD = .85; t(139) = 12.96, p < .001; d = .88). In the next study, two hundred valid samples were recruited from survey company in China (M_age = 27.87 years, SD = 6.68 years; 55% female) and the participants were randomly assigned to two conditions (AI vs. human). After viewing the stimuli of human versus AI, participants are informed that one insurance company would determine the price purely based on their declaration. Therefore, their open-ended answers were coded into ethical, honest behavior and unethical, dishonest behavior according to the design of prior literature. A Chi-square analysis revealed that 64% of the participants would immorally lie towards AI insurance inspector while 42% of participants reported deliberately lower mileage facing with human inspector (χ^2 (1) = 9.71, p = .002). Similarly, the logistic regression results suggested that people would significantly more likely to report fraudulent answer when facing with AI (β = .89, odds ratio = 2.45, Wald = 9.56, p = .002). It is demonstrated that people would be more likely to behave unethically in front of non-human agents, such as AI agent, rather than human. The research findings shed light on new practical ethical issues in human-AI interaction and address the important role of human employees during the process of service delivery in the new era of AI.

Keywords: AI agent, consumer morality, ethical behavior, human-AI interaction

Procedia PDF Downloads 57

Authors: Sittampalam Manoharan, Gary Chai, Sanaul Chowdhury, Andrew Golding

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The primary objective of this paper is to present a simplified approach to develop the structural deterioration model using traffic speed deflectometer data for flexible pavements. Maintaining assets to meet functional performance is not economical or sustainable in the long terms, and it would end up needing much more investments for road agencies and extra costs for road users. Performance models have to be included for structural and functional predicting capabilities, in order to assess the needs, and the time frame of those needs. As such structural modelling plays a vital role in the prediction of pavement performance. A structural condition is important for the prediction of remaining life and overall health of a road network and also major influence on the valuation of road pavement. Therefore, the structural deterioration model is a critical input into pavement management system for predicting pavement rehabilitation needs accurately. The Traffic Speed Deflectometer (TSD) is a vehicle-mounted Doppler laser system that is capable of continuously measuring the structural bearing capacity of a pavement whilst moving at traffic speeds. The device’s high accuracy, high speed, and continuous deflection profiles are useful for network-level applications such as predicting road rehabilitations needs and remaining structural service life. The methodology adopted in this model by utilizing time series TSD maximum deflection (D0) data in conjunction with rutting, rutting progression, pavement age, subgrade strength and equivalent standard axle (ESA) data. Then, regression analyses were undertaken to establish a correlation equation of structural deterioration as a function of rutting, pavement age, seal age and equivalent standard axle (ESA). This study developed a simple structural deterioration model which will enable to incorporate available TSD structural data in pavement management system for developing network-level pavement investment strategies. Therefore, the available funding can be used effectively to minimize the whole –of- life cost of the road asset and also improve pavement performance. This study will contribute to narrowing the knowledge gap in structural data usage in network level investment analysis and provide a simple methodology to use structural data effectively in investment decision-making process for road agencies to manage aging road assets.

Keywords: adjusted structural number (SNP), maximum deflection (D0), equant standard axle (ESA), traffic speed deflectometer (TSD)

Procedia PDF Downloads 132

Authors: Golnush Masghati-Amoli, Paul Chin

Abstract:

Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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