Search results for: precision feed

Authors: Jefriza, Habibah Lateh, Saumi Syahreza

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The 2016 Mw 6.5 Pidie Jaya earthquake is one of the biggest disasters that has occurred in Aceh within the last five years. This earthquake has caused severe damage to many infrastructures such as schools, hospitals, mosques, and houses in the district of Pidie Jaya and surrounding areas. Earthquakes commonly occur in Aceh Province due to the Aceh-Sumatra is located in the convergent boundaries of the Sunda Plate subducted beneath the Indo-Australian Plate. This convergence is responsible for the intensification of seismicity in this region. The plates are tilted at a speed of 63 mm per year and the right lateral component is accommodated by strike- slip faulting within Sumatra, mainly along the great Sumatran fault. This paper presents preliminary findings of InSAR study aimed at investigating the co-seismic surface deformation pattern in Pidie Jaya, Aceh-Indonesia. Coseismic surface deformation is rapid displacement that occurs at the time of an earthquake. Coseismic displacement mapping is required to study the behavior of seismic faults. InSAR is a powerful tool for measuring Earth surface deformation to a precision of a few centimetres. In this study, two radar images of the same area but at two different times are required to detect changes in the Earth’s surface. The ascending and descending Sentinel-1A (S1A) synthetic aperture radar (SAR) data and Sentinels application platform (SNAP) toolbox were used to generate SAR interferogram image. In order to visualize the InSAR interferometric, the S1A from both master (26 Nov 2016) and slave data-sets (26 Dec 2016) were utilized as the main data source for mapping the coseismic surface deformation. The results show that the fringes of phase difference have appeared in the border region as a result of the movement that was detected with interferometric technique. On the other hand, the dominant fringes pattern also appears near the coastal area, this is consistent with the field investigations two days after the earthquake. However, the study has also limitations of resolution and atmospheric artefacts in SAR interferograms. The atmospheric artefacts are caused by changes in the atmospheric refractive index of the medium, as a result, has limitation to produce coherence image. Low coherence will be affected the result in creating fringes (movement can be detected by fringes). The spatial resolution of the Sentinel satellite has not been sufficient for studying land surface deformation in this area. Further studies will also be investigated using both ALOS and TerraSAR-X. ALOS and TerraSAR-X improved the spatial resolution of SAR satellite.

Keywords: earthquake, InSAR, interferometric, Sentinel-1A

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Authors: Dino Carpentras, Alejandro Dinkelberg, Michael Quayle

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Within agent-based modeling, opinion dynamics is the field that focuses on modeling people's opinions. In this prolific field, most of the literature is dedicated to the exploration of the two 'degrees of freedom' and how they impact the model’s properties (e.g., the average final opinion, the number of final clusters, etc.). These degrees of freedom are (1) the interaction rule, which determines how agents update their own opinion, and (2) the network topology, which defines the possible interaction among agents. In this work, we show that the third degree of freedom exists. This can be used to change a model's output up to 100% of its initial value or to transform two models (both from the literature) into each other. Since opinion dynamics models are representations of the real world, it is fundamental to understand how people’s opinions can be measured. Even for abstract models (i.e., not intended for the fitting of real-world data), it is important to understand if the way of numerically representing opinions is unique; and, if this is not the case, how the model dynamics would change by using different representations. The process of measuring opinions is non-trivial as it requires transforming real-world opinion (e.g., supporting most of the liberal ideals) to a number. Such a process is usually not discussed in opinion dynamics literature, but it has been intensively studied in a subfield of psychology called psychometrics. In psychometrics, opinion scales can be converted into each other, similarly to how meters can be converted to feet. Indeed, psychometrics routinely uses both linear and non-linear transformations of opinion scales. Here, we analyze how this transformation affects opinion dynamics models. We analyze this effect by using mathematical modeling and then validating our analysis with agent-based simulations. Firstly, we study the case of perfect scales. In this way, we show that scale transformations affect the model’s dynamics up to a qualitative level. This means that if two researchers use the same opinion dynamics model and even the same dataset, they could make totally different predictions just because they followed different renormalization processes. A similar situation appears if two different scales are used to measure opinions even on the same population. This effect may be as strong as providing an uncertainty of 100% on the simulation’s output (i.e., all results are possible). Still, by using perfect scales, we show that scales transformations can be used to perfectly transform one model to another. We test this using two models from the standard literature. Finally, we test the effect of scale transformation in the case of finite precision using a 7-points Likert scale. In this way, we show how a relatively small-scale transformation introduces both changes at the qualitative level (i.e., the most shared opinion at the end of the simulation) and in the number of opinion clusters. Thus, scale transformation appears to be a third degree of freedom of opinion dynamics models. This result deeply impacts both theoretical research on models' properties and on the application of models on real-world data.

Keywords: degrees of freedom, empirical validation, opinion scale, opinion dynamics

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Authors: H. Scholz, P. Kuehne, G. Heckenberger

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Dietary Anion-Cation Balance (DCAB) in grazing systems under German conditions has a tendency to decrease from May until September and often are measured DCAB lower than 100 meq per kg dry matter. Lower DCAB in grass feeding system can change the metabolic status of suckler cows and often are results in acidotic metabolism. Measurement of acid-base excretion in dairy cows has been proved to a method to evaluate the acid-base status. The hypothesis was that metabolic imbalances could be identified by urine measurement in suckler cows. The farm study was conducted during the grazing seasons 2017 and 2018 and involved 7 suckler cow farms in Germany. Suckler cows were grazing during the whole time of the investigation and had no access to other feeding components. Cows had free access to water and salt block and free access to minerals (loose). The dry matter of the grass was determined at 60 °C and were then analysed for energy and nutrient content and for the Dietary Cation-Anion Balance (DCAB). Urine was collected in 50 ml-glasses and analysed for net acid-base excretion (NSBA) and the concentration of creatinine and urea in the laboratory. Statistical analysis took place with ANOVA with fixed effects of farms (1-7), month (May until September), and number of lactations (1, 2, and ≥ 3 lactations) using SPSS Version 25.0 for windows. An alpha of 0.05 was used for all statistical tests. During the grazing periods of years 2017 and 2018, an average DCAB was observed in the grass of 167 meq per kg DM. A very high mean variation could be determined from -42 meq/kg to +439 meq/kg. Reference values in relation to DCAB were described between 150 meq and 400 meq per kg DM. It was found the high chlorine content with reduced potassium level led to this reduction in DCAB at the end of the grazing period. Between the DCAB of the grass and the NSBA in urine of suckler cows was a correlation according to PEARSON of r = 0.478 (p ≤ 0.001) or after SPEARMAN of r = 0.601 (p ≤ 0.001) observed. For the control of urine values of grazing suckler cows, the wide spread of the values poses a challenge of the interpretation, especially since the DCAB is unknown. The influence of several feeding components such as chlorine, sulfur, potassium, and sodium (ions for the DCAB) and dry matter feed intake during the grazing period of suckler cows should be taken into account in further research. The results obtained show that up a decrease in the DCAB is related to a decrease in NSBA in urine of suckler cows. Monitoring of metabolic disturbances should include analysis of urine, blood, milk, and ruminal fluid.

Keywords: dietary anion-cation balance, DCAB, net acid-base excretion, NSBA, suckler cow, grazing period

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Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

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The utilization of Large Eddy Simulation (LES) has been extensive in turbulence research. LES concentrates on resolving the significant grid-scale motions while representing smaller scales through subfilter-scale (SFS) models. The deconvolution model, among the available SFS models, has proven successful in LES of engineering and geophysical flows. Nevertheless, the thorough investigation of how sub-filter scale dynamics and filter anisotropy affect SFS modeling accuracy remains lacking. The outcomes of LES are significantly influenced by filter selection and grid anisotropy, factors that have not been adequately addressed in earlier studies. This study examines two crucial aspects of LES: Firstly, the accuracy of direct deconvolution models (DDM) is evaluated concerning sub-filter scale (SFS) dynamics across varying filter-to-grid ratios (FGR) in isotropic turbulence. Various invertible filters are employed, including Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The importance of FGR becomes evident as it plays a critical role in controlling errors for precise SFS stress prediction. When FGR is set to 1, the DDM models struggle to faithfully reconstruct SFS stress due to inadequate resolution of SFS dynamics. Notably, prediction accuracy improves when FGR is set to 2, leading to accurate reconstruction of SFS stress, except for cases involving Helmholtz I and II filters. Remarkably high precision, nearly 100%, is achieved at an FGR of 4 for all DDM models. Furthermore, the study extends to filter anisotropy and its impact on SFS dynamics and LES accuracy. By utilizing the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with anisotropic filters, aspect ratios (AR) ranging from 1 to 16 are examined in LES filters. The results emphasize the DDM’s proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. Notably high correlation coefficients exceeding 90% are observed in the a priori study for the DDM’s reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as filter anisotropy increases. In the a posteriori analysis, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, including velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strainrate tensors, and SFS stress. It is evident that as filter anisotropy intensifies, the results of DSM and DMM deteriorate, while the DDM consistently delivers satisfactory outcomes across all filter-anisotropy scenarios. These findings underscore the potential of the DDM framework as a valuable tool for advancing the development of sophisticated SFS models for LES in turbulence research.

Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence

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Authors: Catariny C. Aleman, Flavio B. Campos, Matheus A. Caliman, Everardo C. Mantovani

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The seedling production stage is a critical point in the vegetable production system. Obtaining high-quality seedlings is a prerequisite for subsequent cropping to occur well and productivity optimization is required. The water management is an important step in agriculture production. The adequate water requirement in horticulture seedlings can provide higher quality and increase field production. The practice of irrigation is indispensable and requires a duly adjusted quality irrigation system, together with a specific water management plan to meet the water demand of the crop. Irrigation management in seedling management requires a great deal of specific information, especially when it involves the use of inputs such as hydrorentering polymers and automation technologies of the data acquisition and irrigation system. The experiment was conducted in a greenhouse at the Federal University of Viçosa, Viçosa - MG. Tomato seedlings (Lycopersicon esculentum Mill) were produced in plastic trays of 128 cells, suspended at 1.25 m from the ground. The seedlings were irrigated by 4 micro sprinklers of fixed jet 360º per tray, duly isolated by sideboards, following the methodology developed for this work. During Phase 1, in January / February 2017 (duration of 24 days), the cultivation coefficient (Kc) of seedlings cultured in the presence and absence of hydrogel was evaluated by weighing lysimeter. In Phase 2, September 2017 (duration of 25 days), the seedlings were submitted to 4 irrigation managements (Kc, timer, 0.50 ETo, and 1.00 ETo), in the presence and absence of hydrogel and then evaluated in relation to quality parameters. The microclimate inside the greenhouse was monitored with the use of air temperature, relative humidity and global radiation sensors connected to a microcontroller that performed hourly calculations of reference evapotranspiration by Penman-Monteith standard method FAO56 modified for the balance of long waves according to Walker, Aldrich, Short (1983), and conducted water balance and irrigation decision making for each experimental treatment. Kc of seedlings cultured on a substrate with hydrogel (1.55) was higher than Kc on a pure substrate (1.39). The use of the hydrogel was a differential for the production of earlier tomato seedlings, with higher final height, the larger diameter of the colon, greater accumulation of a dry mass of shoot, a larger area of crown projection and greater the rate of relative growth. The handling 1.00 ETo promoted higher relative growth rate.

Keywords: automatic system; efficiency of water use; precision irrigation, micro sprinkler.

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Authors: Vanessa Casadella, Mourad Chouki, Agnès Ceccarelli, Sofiane Tahi

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Mentoring is positioned in an interpersonal and intergenerational perspective, serving the transmission of interpersonal skills and organizational culture. It echoes orientation, project, self-actualization, guidance, transmission, and filiation. It is available using a formal or informal approach. The formal dimension refers to a privileged relationship between a senior and a junior. Informal mentoring is unplanned and emerges naturally between two people who choose each other. However, it remains more difficult to understand. To study the link between formal and informal mentoring and to define the notion of “social” mentoring, we conducted a qualitative study of an exploratory nature with around ten SSE organizations located in the southeast region of Tunisia. The wealth of this territory has pushed residents to found SSE organizations with a view to creating jobs but also to preserving traditions and preserving nature. These organizations developed spontaneously to solve various local problems, such as the revitalization of deserted rural areas, environmental degradation, and the reskilling and professional reintegration of people marginalized in the labor market. This research, based on semi-structured interviews in order to obtain exhaustive and sensitive data, involves an interview guide with few questions mobilized to let the respondents, leaders of the different structures, express themselves freely. The guide includes questions on activities, methods of sharing knowledge, and difficulties in understanding between stakeholders. The interviews, lasting 30 to 60 minutes, were recorded using a dictaphone and then transcribed in full. The results are as follows: 1. We see two iterative mentoring loops. A first loop can be considered a type of formal mentoring. It highlights the support organized (in the form of training) by social enterprises with the aim of developing the autonomy, know-how, and interpersonal skills of members. A second loop concerns informal mentoring. This is non-formalized support provided by members or with other members of the entourage. This informal mentoring is mainly based on the observation of good practices and learning by doing. 2. We notice an intersection between the two loops. If the first loop is not done, the second will not take place. The knowledge acquired in the first loop is used to feed the second. 3. We note a form of reluctance on the part of some members to share their knowledge for reasons of competition. Ultimately, we retain the notion of “social” mentoring as a hybridization of formal and informal mentoring while dimensioning the “social” perspective by emphasizing the reciprocal character, solidarity, confidence, and trust between the mentor and the mentee.

Keywords: social innovation, social mentoring, social and solidarity economy, informal mentoring

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Authors: Arjun Goutham, Chaitanya Sridhar, Sunita Maheshwari, Robin Uthappa, Prasanna Gopinath

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In alignment with the growth in the sporting industry, a number of people playing and competing in sports are growing exponentially across the globe. However, the number of sports psychology experts are not growing at a similar rate, especially in the Asian and more so, Indian context. Hence, the access to actionable mental training solutions specific to individual athletes is limited. Also, the time constraint an athlete faces due to their intense training schedule makes one-on-one sessions difficult. One of the means to bridge that gap is through technology. Technology makes individualization possible. It allows for easy access to specific-qualitative content/information and provides a medium to place individualized assessments, analysis, solutions directly into an athlete's hands. This enables mental training awareness, education, and real-time actionable solutions possible for athletes in-spite of the limitation of available sports psychology experts in their region. Furthermore, many athletes are hesitant to seek support due to the stigma of appearing weak. Such individuals would prefer a more discreet way. Athletes who have strong mental performance tend to produce better results. The mobile application helps to equip athletes with assessing and developing their mental strategies directed towards improving performance on an ongoing basis. When an athlete understands their strengths and limitations in their mental application, they can focus specifically on applying the strategies that work and improve on zones of limitation. With reports, coaches get to understand the unique inner workings of an athlete and can utilize the data & analysis to coach them with better precision and use coaching styles & communication that suits better. Systematically capturing data and supporting athletes(with individual-specific solutions) or teams with assessment, planning, instructional content, actionable tools & strategies, reviewing mental performance and the achievement of objectives & goals facilitate for a consistent mental skills development at all levels of sporting stages of an athlete's career. The mobile application will help athletes recognize and align with their stable attributes such as their personalities, learning & execution modalities, challenges & requirements of their sport, etc and help develop dynamic attributes like states, beliefs, motivation levels, focus etc. with practice and training. It will provide measurable analysis on a regular basis and help them stay aligned to their objectives & goals. The solutions are based on researched areas of influence on sporting performance individually or in teams.

Keywords: athletes, mental training, mobile application, performance, sports

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Authors: O. B. Iskender, K. V. Ling, V. Dubanchet, L. Simonini

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This paper discusses the recent advances and future prospects of spacecraft position and attitude control using Model Predictive Control (MPC). First, the challenges of the space missions are summarized, in particular, taking into account the errors, uncertainties, and constraints imposed by the mission, spacecraft and, onboard processing capabilities. The summary of space mission errors and uncertainties provided in categories; initial condition errors, unmodeled disturbances, sensor, and actuator errors. These previous constraints are classified into two categories: physical and geometric constraints. Last, real-time implementation capability is discussed regarding the required computation time and the impact of sensor and actuator errors based on the Hardware-In-The-Loop (HIL) experiments. The rationales behind the scenarios’ are also presented in the scope of space applications as formation flying, attitude control, rendezvous and docking, rover steering, and precision landing. The objectives of these missions are explained, and the generic constrained MPC problem formulations are summarized. Three key design elements used in MPC design: the prediction model, the constraints formulation and the objective cost function are discussed. The prediction models can be linear time invariant or time varying depending on the geometry of the orbit, whether it is circular or elliptic. The constraints can be given as linear inequalities for input or output constraints, which can be written in the same form. Moreover, the recent convexification techniques for the non-convex geometrical constraints (i.e., plume impingement, Field-of-View (FOV)) are presented in detail. Next, different objectives are provided in a mathematical framework and explained accordingly. Thirdly, because MPC implementation relies on finding in real-time the solution to constrained optimization problems, computational aspects are also examined. In particular, high-speed implementation capabilities and HIL challenges are presented towards representative space avionics. This covers an analysis of future space processors as well as the requirements of sensors and actuators on the HIL experiments outputs. The HIL tests are investigated for kinematic and dynamic tests where robotic arms and floating robots are used respectively. Eventually, the proposed algorithms and experimental setups are introduced and compared with the authors' previous work and future plans. The paper concludes with a conjecture that MPC paradigm is a promising framework at the crossroads of space applications while could be further advanced based on the challenges mentioned throughout the paper and the unaddressed gap.

Keywords: convex optimization, model predictive control, rendezvous and docking, spacecraft autonomy

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Authors: Norazlan Mohmad Misnan, Maizatul Hasyima Omar, Mohd Isa Wasiman

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Papaya (Carica papaya L., Caricaceae) is a tree which mainly cultivated for its fruits in many tropical regions including Australia, Brazil, China, Hawaii, and Malaysia. Beside of fruits, its leaves, seeds, and latex have also been traditionally used for treating diseases, which also reported to possess anti-cancer and anti- malaria properties. Its leaves have been reported to consist of various chemical compounds such as alkaloids, flavonoids and phenolics. Clitorin and manghaslin are among major flavonoids presence. Thus, the aim of this study is to quantify the purity of these isolated compounds (clitorin and manghsalin) by using quantitative Nuclear Magnetic Resonance (qNMR) analysis. Only fresh C. papaya leaves were used for juice extraction procedure and subsequently was freeze-dried to obtain a dark green powdered form of the extract prior to Centrifugal Partition Chromatography (CPC) separation. The CPC experiments were performed using a two-phase solvent system comprising ethyl acetate/butanol/water (1:4:5, v/v/v/v) solvent. The upper organic phase was used as the stationary phase, and the lower aqueous phase was employed as the mobile phase. Ten fractions were obtained after an hour runtime analysis. Fraction 6 and fraction 8 has been identified as clitorin (m/z 739.21 [M-H]-) and manghaslin (m/z 755.21 [M-H]-), respectively, based on LCMS data and full analysis of NMR (1H NMR, 13C NMR, HMBC, and HSQC). The 1H-qNMR measurements were carried out using a 400 MHz NMR spectrometer (JEOL ECS 400MHz, Japan) and deuterated methanol was used as a solvent. Quantification was performed using the AQARI method (Accurate Quantitative NMR) with deuterated 1,4-Bis(trimethylsilyl)benzene (BTMSB) as an internal reference substances. This AQARI protocol includes not only NMR measurement but also sample preparation that provide highest precision and accuracy than other qNMR methods. The 90° pulse length and the T1 relaxation times for compounds and BTMSB were determined prior to the quantification to give the best signal-to-noise ratio. Regions containing the two downfield signals from aromatic part (6.00–6.89 ppm), and the singlet signal, (18H) arising from BTMSB (0.63-1.05ppm) were selected for integration. The purity of clitorin and manghaslin were calculated to be 52.22% and 43.36%, respectively. Further purification is needed in order to increase its purity. This finding has demonstrated the use of qNMR for quality control and standardization of various plant extracts and which can be applied for NMR fingerprinting of other plant-based products with good reproducibility and in the case where commercial standards is not readily available.

Keywords: Carica papaya, clitorin, manghaslin, quantitative Nuclear Magnetic Resonance, Centrifugal Partition Chromatography

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Authors: Chen Yuan, Nick Green, Stuart Blackburn

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The investment casting process offers a great freedom of design combined with the economic advantage of near net shape manufacturing. It is widely used for the production of high value precision cast parts in particularly in the aerospace sector. Various combinations of materials have been used to produce the ceramic moulds, but most investment foundries use a silica based binder system in conjunction with fused silica, zircon, and alumino-silicate refractories as both filler and coarse stucco materials. However, in the context of advancing alloy technologies, silica based systems are struggling to keep pace, especially when net-shape casting titanium alloys. Study has shown that the casting of titanium based alloys presents considerable problems, including the extensive interactions between the metal and refractory, and the majority of metal-mould interaction is due to reduction of silica, present as binder and filler phases, by titanium in the molten state. Cleaner, more refractory systems are being devised to accommodate these changes. Although yttria has excellent chemical inertness to titanium alloy, it is not very practical in a production environment combining high material cost, short slurry life, and poor sintering properties. There needs to be a cost effective solution to these issues. With limited options for using pure oxides, in this work, a silica-free magnesia spinel MgAl₂O₄ was used as a primary coat filler and alumina as a binder material to produce facecoat in the investment casting mould. A comparison system was also studied with a fraction of the rare earth oxide Y₂O₃ adding into the filler to increase the inertness. The stability of the MgAl₂O₄/Al₂O₃ and MgAl₂O₄/Y₂O₃/Al₂O₃ slurries was assessed by tests, including pH, viscosity, zeta-potential and plate weight measurement, and mould properties such as friability were also measured. The interaction between the face coat and titanium alloy was studied by both a flash re-melting technique and a centrifugal investment casting method. The interaction products between metal and mould were characterized using x-ray diffraction (XRD), scanning electron microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDS). The depth of the oxygen hardened layer was evaluated by micro hardness measurement. Results reveal that introducing a fraction of Y₂O₃ into magnesia spinel can significantly increase the slurry life and reduce the thickness of hardened layer during centrifugal casting.

Keywords: titanium alloy, mould, MgAl₂O₄, Y₂O₃, interaction, investment casting

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Authors: Sahar Heidary

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Radiography and radiotherapy have emerged as crucial pillars of modern medical practice, revolutionizing diagnostics and treatment for a myriad of health conditions. This abstract highlights the pivotal role of radiography and radiotherapy in favor of healthcare and society. Radiography, a non-invasive imaging technique, has significantly advanced medical diagnostics by enabling the visualization of internal structures and abnormalities within the human body. With the advent of digital radiography, clinicians can obtain high-resolution images promptly, leading to faster diagnoses and informed treatment decisions. Radiography plays a pivotal role in detecting fractures, tumors, infections, and various other conditions, allowing for timely interventions and improved patient outcomes. Moreover, its widespread accessibility and cost-effectiveness make it an indispensable tool in healthcare settings worldwide. On the other hand, radiotherapy, a branch of medical science that utilizes high-energy radiation, has become an integral component of cancer treatment and management. By precisely targeting and damaging cancerous cells, radiotherapy offers a potent strategy to control tumor growth and, in many cases, leads to cancer eradication. Additionally, radiotherapy is often used in combination with surgery and chemotherapy, providing a multifaceted approach to combat cancer comprehensively. The continuous advancements in radiotherapy techniques, such as intensity-modulated radiotherapy and stereotactic radiosurgery, have further improved treatment precision while minimizing damage to surrounding healthy tissues. Furthermore, radiography and radiotherapy have demonstrated their worth beyond oncology. Radiography is instrumental in guiding various medical procedures, including catheter placement, joint injections, and dental evaluations, reducing complications and enhancing procedural accuracy. On the other hand, radiotherapy finds applications in non-cancerous conditions like benign tumors, vascular malformations, and certain neurological disorders, offering therapeutic options for patients who may not benefit from traditional surgical interventions. In conclusion, radiography and radiotherapy stand as indispensable tools in modern medicine, driving transformative improvements in patient care and treatment outcomes. Their ability to diagnose, treat, and manage a wide array of medical conditions underscores their favor in medical practice. As technology continues to advance, radiography and radiotherapy will undoubtedly play an ever more significant role in shaping the future of healthcare, ultimately saving lives and enhancing the quality of life for countless individuals worldwide.

Keywords: radiology, radiotherapy, medical imaging, cancer treatment

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Authors: Y. Muto, S. Araki, H. Yamamoto

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The separation of oxygen is one key technology to improve the efficiency and to reduce the cost for the processed of the partial oxidation of the methane and the condensation of the carbon dioxide. Particularly, carbon dioxide at high concentration would be obtained by the combustion using pure oxygen separated from air. However, the oxygen separation process occupied the large part of energy consumption. Therefore, it is considered that the membrane technologies enable to separation at lower cost and lower energy consumption than conventional methods. In this study, it is examined that the separation of oxygen using membranes of mixed conductors. Oxygen permeation through the membrane is occurred by the following three processes. At first, the oxygen molecules dissociate into oxygen ion at feed side of the membrane, subsequently, oxygen ions diffuse in the membrane. Finally, oxygen ions recombine to form the oxygen molecule. Therefore, it is expected that the membrane of thickness and material, or catalysts of the dissociation and recombination affect the membrane performance. However, there is little article about catalysts for the dissociation and recombination. We confirmed the performance of La0.6Sr0.4Co1.0O3-δ (LSC) based catalyst which was commonly used as the dissociation and recombination. It is known that the adsorbed amount of oxygen increase with the increase of doped Fe content in B site of LSC. We prepared the catalysts of La0.1Sr0.9Co0.9Fe0.1O3-δ(C9F1), La0.1Sr0.9Co0.5Fe0.5O3-δ(C5F5) and La0.1Sr0.9Co0.3Fe0.7O3-δ(C7F3). Also, we used Pr2NiO4 type mixed conductor as a membrane material. (Pr0.9La0.1)2(Ni0.74Cu0.21Ga0.05)O4+δ(PLNCG) shows the high oxygen permeability and the stability against carbon dioxide. Oxygen permeation experiments were carried out using a homemade apparatus at 850 -975 °C. The membrane was sealed with Pyrex glass at both end of the outside dense alumina tubes. To measure the oxygen permeation rate, air was fed to the film side at 50 ml min-1, helium as the sweep gas and reference gas was fed at 20 ml min-1. The flow rates of the sweep gas and the gas permeated through the membrane were measured using flow meter and the gas concentrations were determined using a gas chromatograph. Then, the permeance of the oxygen was determined using the flow rate and the concentration of the gas on the permeate side of the membrane. The increase of oxygen permeation was observed with increasing temperature. It is considered that this is due to the catalytic activities are increased with increasing temperature. Another reason is the increase of oxygen diffusivity in the bulk of membrane. The oxygen permeation rate is improved by using catalyst of LSC or LSCF. The oxygen permeation rate of membrane with LSCF showed higher than that of membrane with LSC. Furthermore, in LSCF catalysts, oxygen permeation rate increased with the increase of the doped amount of Fe. It is considered that this is caused by the increased of adsorbed amount of oxygen.

Keywords: membrane separation, oxygen permeation, K2NiF4-type structure, mixed conductor

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Authors: Yuli Andrea P. Bermudez, Richard R. Lobo, Tamyres R. D. Amorim, Danny Alexander R. Moreno, Angelica Simone C. Pereira, Ives Claudio D. Bueno

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The use of natural antioxidants in animal feed can positively modify the profile of fatty acids (FAs) in meat, due to the presence of secondary metabolites, mainly phenolic and flavonoid compounds, which promote an increase in the associated polyunsaturated fatty acids (PUFA) with beneficial factors in human health. The goal of this study was to evaluate the effect of the dietary inclusion percentage of yerba mate extract (Ilex paraguariensis St. Hilaire) as a natural antioxidant on lamb meat quality. The animals were confined for 53 days and fed with corn silage and concentrated in the proportion of 60:40, respectively, were divided into four homogeneous groups (n = 9 lambs/group), to each of the treatments, one control group without yerba mate extract - YME (0%) and three treatments with 1, 2 and 4% the inclusion of YME on a DM basis. Samples of the Longissimus thoracis (LT) muscle were collected from the deboning of 36 lambs, analyzing pH values, color parameters (brightness: L*, red value: a*, and yellow: b*), fatty acid profile, total lipids, and sensory analysis. The inclusion of YME modified the value of b* (P = 0.0041), indicating a higher value of yellow color in the meat, for the group supplemented with 4% YME. All data were statistically evaluated using the MIXED procedure of the statistical package SAS 9.4. However, it did not show differences in the final live weight in the groups evaluated, as well as in the pH values (P = 0.1923) and the total lipid concentration (P = 0.0752). The FAs (P ≥ 0.1360) and health indexes were not altered by the inclusion of YME (P ≥ 0.1360); only branched-chain fatty acids (BCFA) exhibited a diet effect (P = 0.0092) in the group that had 4% of the extract. In the sensory analysis test with a hedonic scale it did not show differences between the treatments (P ≥ 0.1251). Nevertheless, in the just about-right test, using (note 1) to 'very strong, softness or moist' (note 5); the softness was different between the evaluated treatments (P = 0.0088) where groups with 2% YME had a better acceptance of tasters (4.15 ± 0.08) compared to the control (3.89 ± 0.08). In conclusion, although the addition of YME has shown positive results in sensory acceptance and in increasing the concentration of BCFA, fatty acids beneficial to human health, without changing the physical-chemical parameters in lamb meat, the absolute changes are considered to have been quite small, which was probably related to the high efficiency of PUFA biohydrogenation in the n the rumen.

Keywords: composition, health, antioxidant, meat analysis

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Authors: Elena E. Lyakso, Olga V. Frolova, Yuri N. Matveev, Aleksey S. Grigorev, Alexander S. Nikolaev, Viktor A. Gorodnyi

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The study of the children’s emotional sphere depending on age and psychoneurological state is of great importance for the design of educational programs for children and their social adaptation. Atypical development may be accompanied by violations or specificities of the emotional sphere. To study characteristics of the emotional state reflection in the voice and speech features of children, the perceptual study with the participation of adults and the automatic recognition of speech were conducted. Speech of children with typical development (TD), with Down syndrome (DS), and with autism spectrum disorders (ASD) aged 6-12 years was recorded. To obtain emotional speech in children, model situations were created, including a dialogue between the child and the experimenter containing questions that can cause various emotional states in the child and playing with a standard set of toys. The questions and toys were selected, taking into account the child’s age, developmental characteristics, and speech skills. For the perceptual experiment by adults, test sequences containing speech material of 30 children: TD, DS, and ASD were created. The listeners were 100 adults (age 19.3 ± 2.3 years). The listeners were tasked with determining the children’s emotional state as “comfort – neutral – discomfort” while listening to the test material. Spectrographic analysis of speech signals was conducted. For automatic recognition of the emotional state, 6594 speech files containing speech material of children were prepared. Automatic recognition of three states, “comfort – neutral – discomfort,” was performed using automatically extracted from the set of acoustic features - the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) and the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS). The results showed that the emotional state is worse determined by the speech of TD children (comfort – 58% of correct answers, discomfort – 56%). Listeners better recognized discomfort in children with ASD and DS (78% of answers) than comfort (70% and 67%, respectively, for children with DS and ASD). The neutral state is better recognized by the speech of children with ASD (67%) than by the speech of children with DS (52%) and TD children (54%). According to the automatic recognition data using the acoustic feature set GeMAPSv01b, the accuracy of automatic recognition of emotional states for children with ASD is 0.687; children with DS – 0.725; TD children – 0.641. When using the acoustic feature set eGeMAPSv01b, the accuracy of automatic recognition of emotional states for children with ASD is 0.671; children with DS – 0.717; TD children – 0.631. The use of different models showed similar results, with better recognition of emotional states by the speech of children with DS than by the speech of children with ASD. The state of comfort is automatically determined better by the speech of TD children (precision – 0.546) and children with ASD (0.523), discomfort – children with DS (0.504). The data on the specificities of recognition by adults of the children’s emotional state by their speech may be used in recruitment for working with children with atypical development. Automatic recognition data can be used to create alternative communication systems and automatic human-computer interfaces for social-emotional learning. Acknowledgment: This work was financially supported by the Russian Science Foundation (project 18-18-00063).

Keywords: autism spectrum disorders, automatic recognition of speech, child’s emotional speech, Down syndrome, perceptual experiment

Procedia PDF Downloads 186

Authors: Claudio Lamilla, Andrés Santos, Vicente Llanquinao, Jocelyn Hermosilla, Leticia Barrientos

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The industry in general is very interested in improving and optimizing industrial processes in order to reduce the costs involved in obtaining raw materials and production. Thus, an interesting and cost-effective alternative is the incorporation of bioactive metabolites in such processes, being an example of this enzymes which catalyze efficiently a large number of enzymatic reactions of industrial and biotechnological interest. In the search for new sources of these active metabolites, Antarctica is one of the least explored places on our planet where the most drastic cold conditions, salinity, UVA-UVB and liquid water available are present, features that have shaped all life in this very harsh environment, especially bacteria that live in different Antarctic ecosystems, which have had to develop different strategies to adapt to these conditions, producing unique biochemical strategies. In this work the production of cellulolytic enzymes of seven bacterial strains isolated from marine sediments at different sites in the Antarctic was evaluated. Isolation of the strains was performed using serial dilutions in the culture medium at M115°C. The identification of the strains was performed using universal primers (27F and 1492R). The enzyme activity assays were performed on R2A medium, carboxy methyl cellulose (CMC)was added as substrate. Degradation of the substrate was revealed by adding Lugol. The results show that four of the tested strains produce enzymes which degrade CMC substrate. The molecular identifications, showed that these bacteria belong to the genus Streptomyces and Pseudoalteromonas, being Streptomyces strain who showed the highest activity. Only some bacteria in marine sediments have the ability to produce these enzymes, perhaps due to their greater adaptability to degrade at temperatures bordering zero degrees Celsius, some algae that are abundant in this environment and have cellulose as the main structure. The discovery of new enzymes adapted to cold is of great industrial interest, especially for paper, textiles, detergents, biofuels, food and agriculture. These enzymes represent 8% of industrial demand worldwide and is expected to increase their demand in the coming years. Mainly in the paper and food industry are required in extraction processes starch, protein and juices, as well as the animal feed industry where treating vegetables and grains helps improve the nutritional value of the food, all this clearly puts Antarctic microorganisms and their enzymes specifically as a potential contribution to industry and the novel biotechnological applications.

Keywords: antarctic, bacteria, biotechnological, cellulolytic enzymes

Procedia PDF Downloads 287

Authors: D. Serero, L. Couton, J. D. Parisse, R. Leroy

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In urban planning, an increasing number of cities require wind analysis to verify comfort of public spaces and around buildings. These studies are made using computer fluid dynamic simulation (CFD). However, this technique is often based on wind information taken from meteorological stations located at several kilometers of the spot of analysis. The approximated input data on project surroundings produces unprecise results for this type of analysis. They can only be used to get general behavior of wind in a zone but not to evaluate precise wind speed. This paper presents another approach to this problem, based on collecting wind data and generating an urban wind cartography using connected ultrasound anemometers. They are wireless devices that send immediate data on wind to a remote server. Assembled in array, these devices generate geo-localized data on wind such as speed, temperature, pressure and allow us to compare wind behavior on a specific site or building. These Netatmo-type anemometers communicate by wifi with central equipment, which shares data acquired by a wide variety of devices such as wind speed, indoor and outdoor temperature, rainfall, and sunshine. Beside its precision, this method extracts geo-localized data on any type of site that can be feedback looped in the architectural design of a building or a public place. Furthermore, this method allows a precise calibration of a virtual wind tunnel using numerical aeraulic simulations (like STAR CCM + software) and then to develop the complete volumetric model of wind behavior over a roof area or an entire city block. The paper showcases connected ultrasonic anemometers, which were implanted for an 18 months survey on four study sites in the Grand Paris region. This case study focuses on Paris as an urban environment with multiple historical layers whose diversity of typology and buildings allows considering different ways of capturing wind energy. The objective of this approach is to categorize the different types of wind in urban areas. This, particularly the identification of the minimum and maximum wind spectrum, helps define the choice and performance of wind energy capturing devices that could be implanted there. The localization on the roof of a building, the type of wind, the altimetry of the device in relation to the levels of the roofs, the potential nuisances generated. The method allows identifying the characteristics of wind turbines in order to maximize their performance in an urban site with turbulent wind.

Keywords: computer fluid dynamic simulation in urban environment, wind energy harvesting devices, net-zero energy building, urban wind behavior simulation, advanced building skin design methodology

Procedia PDF Downloads 98

Authors: Endalkachew Abebe Kebede, Bojin Bojinov, Andon Vasilev Andonov, Orhan Dengiz

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Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study.

Keywords: landsat 9, leaf spectrometer, sentinel 2, UAV

Procedia PDF Downloads 99

Authors: Halimat M. Ajose-Adeogun, Zaynab A. Bello

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Artificial Intelligence (AI) and Natural Language Processing (NLP) have transformed computer language comprehension, allowing computers to comprehend spoken and written language with human-like cognition. NLP, a multidisciplinary area that combines rule-based linguistics, machine learning, and deep learning, enables computers to analyze and comprehend human language. NLP applications in medicine range from tackling issues in electronic health records (EHR) and psychiatry to improving diagnostic precision in orthopedic surgery and optimizing clinical procedures with novel technologies like chatbots. The technology shows promise in a variety of medical sectors, including quicker access to medical records, faster decision-making for healthcare personnel, diagnosing dysplasia in Barrett's esophagus, boosting radiology report quality, and so on. However, successful adoption requires training for healthcare workers, fostering a deep understanding of NLP components, and highlighting the significance of validation before actual application. Despite prevailing challenges, continuous multidisciplinary research and collaboration are critical for overcoming restrictions and paving the way for the revolutionary integration of NLP into medical practice. This integration has the potential to improve patient care, research outcomes, and administrative efficiency. The research methodology includes using NLP techniques for Sentiment Analysis and Emotion Recognition, such as evaluating text or audio data to determine the sentiment and emotional nuances communicated by users, which is essential for designing a responsive and sympathetic chatbot. Furthermore, the project includes the adoption of a Personalized Intervention strategy, in which chatbots are designed to personalize responses by merging NLP algorithms with specific user profiles, treatment history, and emotional states. The synergy between NLP and personalized medicine principles is critical for tailoring chatbot interactions to each user's demands and conditions, hence increasing the efficacy of mental health care. A detailed survey corroborated this synergy, revealing a remarkable 20% increase in patient satisfaction levels and a 30% reduction in workloads for healthcare practitioners. The poll, which focused on health outcomes and was administered to both patients and healthcare professionals, highlights the improved efficiency and favorable influence on the broader healthcare ecosystem.

Keywords: natural language processing, artificial intelligence, healthcare communication, electronic health records, patient care

Procedia PDF Downloads 70

Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

Procedia PDF Downloads 475

Authors: Sheng-Po Tseng, Che-Hua Yang

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Motivation and Objective: Ultrasonic guided waves become an important tool for nondestructive evaluation of structures and components. Guided waves are used for the purpose of identifying defects or evaluating material properties in a nondestructive way. While guided waves are applied for evaluating material properties, instead of knowing the properties directly, preliminary signals such as time domain signals or frequency domain spectra are first revealed. With the measured ultrasound data, inversion calculation can be further employed to obtain the desired mechanical properties. Methods: This research is development of high speed inversion calculation technique for obtaining full-field mechanical properties from the quantitative laser ultrasound visualization system (QLUVS). The quantitative laser ultrasound visualization system (QLUVS) employs a mirror-controlled scanning pulsed laser to generate guided acoustic waves traveling in a two-dimensional target. Guided waves are detected with a piezoelectric transducer located at a fixed location. With a gyro-scanning of the generation source, the QLUVS has the advantage of fast, full-field, and quantitative inspection. Results and Discussions: This research introduces two important tools to improve the computation efficiency. Firstly, graphic procession unit (GPU) with large amount of cores are introduced. Furthermore, combining the CPU and GPU cores, parallel procession scheme is developed for the inversion of full-field mechanical properties based on the QLUVS data. The newly developed inversion scheme is applied to investigate the computation efficiency for single-layered and double-layered plate-like samples. The computation efficiency is shown to be 80 times faster than unparalleled computation scheme. Conclusions: This research demonstrates a high-speed inversion technique for the characterization of full-field material properties based on quantitative laser ultrasound visualization system. Significant computation efficiency is shown, however not reaching the limit yet. Further improvement can be reached by improving the parallel computation. Utilizing the development of the full-field mechanical property inspection technology, full-field mechanical property measured by non-destructive, high-speed and high-precision measurements can be obtained in qualitative and quantitative results. The developed high speed computation scheme is ready for applications where full-field mechanical properties are needed in a nondestructive and nearly real-time way.

Keywords: guided waves, material characterization, nondestructive evaluation, parallel processing

Procedia PDF Downloads 195

Authors: S. Mehrab Amiri, Nasser Talebbeydokhti

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Modeling sediment transport processes by means of numerical approach often poses severe challenges. In this way, a number of techniques have been suggested to solve flow and sediment equations in decoupled, semi-coupled or fully coupled forms. Furthermore, in order to capture flow discontinuities, a number of techniques, like artificial viscosity and shock fitting, have been proposed for solving these equations which are mostly required careful calibration processes. In this research, a numerical scheme for solving shallow water and Exner equations in fully coupled form is presented. First-Order Centered scheme is applied for producing required numerical fluxes and the reconstruction process is carried out toward using Monotonic Upstream Scheme for Conservation Laws to achieve a high order scheme.  In order to satisfy C-property of the scheme in presence of bed topography, Surface Gradient Method is proposed. Combining the presented scheme with fourth order Runge-Kutta algorithm for time integration yields a competent numerical scheme. In addition, to handle non-prismatic channels problems, Cartesian Cut Cell Method is employed. A trained Multi-Layer Perceptron Artificial Neural Network which is of Feed Forward Back Propagation (FFBP) type estimates sediment flow discharge in the model rather than usual empirical formulas. Hydrodynamic part of the model is tested for showing its capability in simulation of flow discontinuities, transcritical flows, wetting/drying conditions and non-prismatic channel flows. In this end, dam-break flow onto a locally non-prismatic converging-diverging channel with initially dry bed conditions is modeled. The morphodynamic part of the model is verified simulating dam break on a dry movable bed and bed level variations in an alluvial junction. The results show that the model is capable in capturing the flow discontinuities, solving wetting/drying problems even in non-prismatic channels and presenting proper results for movable bed situations. It can also be deducted that applying Artificial Neural Network, instead of common empirical formulas for estimating sediment flow discharge, leads to more accurate results.

Keywords: artificial neural network, morphodynamic model, sediment continuity equation, shallow water equations

Procedia PDF Downloads 183

Authors: Ali Ashtiani, Hamid Shirazi

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This paper reviews the status of existing practice and research related to measuring pavement cracking and using crack density as a pavement surface evaluation protocol. Crack density for pavement evaluation is currently not widely used within the airport community and its use by the highway community is limited. However, surface cracking is a distress that is closely monitored by airport staff and significantly influences the development of maintenance, rehabilitation and reconstruction plans for airport pavements. Therefore crack density has the potential to become an important indicator of pavement condition if the type, severity and extent of surface cracking can be accurately measured. A pavement distress survey is an essential component of any pavement assessment. Manual crack surveying has been widely used for decades to measure pavement performance. However, the accuracy and precision of manual surveys can vary depending upon the surveyor and performing surveys may disrupt normal operations. Given the variability of manual surveys, this method has shown inconsistencies in distress classification and measurement. This can potentially impact the planning for pavement maintenance, rehabilitation and reconstruction and the associated funding strategies. A substantial effort has been devoted for the past 20 years to reduce the human intervention and the error associated with it by moving toward automated distress collection methods. The automated methods refer to the systems that identify, classify and quantify pavement distresses through processes that require no or very minimal human intervention. This principally involves the use of a digital recognition software to analyze and characterize pavement distresses. The lack of established protocols for measurement and classification of pavement cracks captured using digital images is a challenge to developing a reliable automated system for distress assessment. Variations in types and severity of distresses, different pavement surface textures and colors and presence of pavement joints and edges all complicate automated image processing and crack measurement and classification. This paper summarizes the commercially available systems and technologies for automated pavement distress evaluation. A comprehensive automated pavement distress survey involves collection, interpretation, and processing of the surface images to identify the type, quantity and severity of the surface distresses. The outputs can be used to quantitatively calculate the crack density. The systems for automated distress survey using digital images reviewed in this paper can assist the airport industry in the development of a pavement evaluation protocol based on crack density. Analysis of automated distress survey data can lead to a crack density index. This index can be used as a means of assessing pavement condition and to predict pavement performance. This can be used by airport owners to determine the type of pavement maintenance and rehabilitation in a more consistent way.

Keywords: airport pavement management, crack density, pavement evaluation, pavement management

Procedia PDF Downloads 183

Authors: Kristina Pflug, Markus Busch

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Being able to predict polymer properties and processing behavior based on the applied operating reaction conditions in one of the key challenges in modern polymer reaction engineering. Especially, for cost-intensive processes such as the high-pressure polymerization of low-density polyethylene (LDPE) with high safety-requirements, the need for simulation-based process optimization and product design is high. A multi-scale modelling approach was set-up and validated via a series of high-pressure mini-plant autoclave reactor experiments. The approach starts with the numerical modelling of the complex reaction network of the LDPE polymerization taking into consideration the actual reaction conditions. While this gives average product properties, the complex polymeric microstructure including random short- and long-chain branching is calculated via a hybrid Monte Carlo-approach. Finally, the processing behavior of LDPE -its melt flow behavior- is determined in dependence of the previously determined polymeric microstructure using the branch on branch algorithm for randomly branched polymer systems. All three steps of the multi-scale modelling approach can be independently validated against analytical data. A triple-detector GPC containing an IR, viscosimetry and multi-angle light scattering detector is applied. It serves to determine molecular weight distributions as well as chain-length dependent short- and long-chain branching frequencies. 13C-NMR measurements give average branching frequencies, and rheological measurements in shear and extension serve to characterize the polymeric flow behavior. The accordance of experimental and modelled results was found to be extraordinary, especially taking into consideration that the applied multi-scale modelling approach does not contain parameter fitting of the data. This validates the suggested approach and proves its universality at the same time. In the next step, the modelling approach can be applied to other reactor types, such as tubular reactors or industrial scale. Moreover, sensitivity analysis for systematically varying process conditions is easily feasible. The developed multi-scale modelling approach finally gives the opportunity to predict and design LDPE processing behavior simply based on process conditions such as feed streams and inlet temperatures and pressures.

Keywords: low-density polyethylene, multi-scale modelling, polymer properties, reaction engineering, rheology

Procedia PDF Downloads 120

Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

Procedia PDF Downloads 138

Authors: Veselin Nanev, Margarita Gabrashanska, Neli Tsocheva-Gaytandzieva

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Ascaridiasis in chicks is one of the major causes for the reduction in body weights, higher mortality, and reduction in egg production, worse meat quantity, pathological lesions, blood losses, and secondary infections. It is responsible for economic losses to the poultry. Despite being economically important parasite, little work has been carried out on the role of antioxidants in the pathogenesis of ascaridiasis. Zinc is a trace elements with multiple functions and one of them is its antioxidant ability. The aim of this study was to investigate the combined effect of organic zinc compound (2Gly.ZnCl22H20) and Ascaridia galli infection on the antioxidant status of broiler chicks. The activity of antioxidant enzymes superoxide dismutase, glutathione peroxidase, the level of lipid peroxidation, expressed by malonyl dialdexyde and plasma zinc in chicks experimentally infected with Ascaridia galli was investigated. Parasite burden was studied as well. The study was performed on 80 broiler chicks, Cobb 500 hybrids. They were divided into four groups – 1st group – control (non-treated and non-infected, 2nd group – infected with embryonated eggs of A. galli and without treatment, 3rd group- only treated with 2Gly.ZnCl22H20 compound and gr. 4 - infected and supplemented with Zn-compound. The chicks in gr. 2 and 4 were infected orally with 450 embryonated eggs of A.galli on day 14 post infection. The chicks from gr. 3 and 4 received 40 mg Zn compound /kg of feed after the 1st week of age during 10 days. All chicks were similarly fed, managed and killed at 60 day p.i. Helminthological, biochemical and statistical methods were applied. Reduced plasma Zn content was observed in the infected chicks compared to controls. Zinc supplementation did not restored the lower Zn content. Cu, Zn-SOD was decreased significantly in the infected chicks compared to controls. The GPx – activity was significantly increased in the infected chicks than the controls. Increased GPx activity together with decreased Cu/ZnSOD activity revealed unbalanced antioxidant defense capacity. The increased MDA level in chicks and changes in the activity of the enzymes showed a development of oxidative stress during the infection with A.galli. Zn compound supplementation has been shown to influence the activity of both antioxidant enzymes (SOD, GPx) and reduced MDA in the infected chicks. Organic zinc supplementation improved the antioxidant defense and protect hosts from oxidant destruction, but without any effect on the parasite burden. The number of helminths was similar in both groups. Zn supplementation did not changed the number of parasites. Administration of oral 2Gly.ZnCl22H20 compound has been shown to be useful in chicks infected with A. galli by its improvement of their antioxidant potential.

Keywords: Ascaridia galli, antioxidants, broiler chicks, zinc supplementation

Procedia PDF Downloads 129

Authors: Haseen Siddiqui, Sanjay M. Mahajani

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Various important reactions in chemical and metallurgical industries fall in the category of gas-solid reactions. These reactions can be categorized as catalytic and non-catalytic gas-solid reactions. In gas-solid reaction systems, heat and mass transfer limitations put an appreciable influence on the rate of the reaction. The consequences can be unavoidable for overlooking such effects while collecting the reaction rate data for the design of the reactor. Pyrolysis reaction comes in this category that involves the production of gases due to the interaction of heat and solid substance. Pyrolysis is also an important step in the gasification process and therefore, the gasification reactivity majorly influenced by the pyrolysis process that produces the char, as a feed for the gasification process. Therefore, in the present study, a non-isothermal transient 1-D model is developed for a single biomass pellet to investigate the effect of heat and mass transfer limitations on the rate of pyrolysis reaction. The obtained set of partial differential equations are firstly discretized using the concept of ‘method of lines’ to obtain a set of ordinary differential equation with respect to time. These equations are solved, then, using MATLAB ode solver ode15s. The model is capable of incorporating structural changes, porosity variation, variation in various thermal properties and various pellet shapes. The model is used to analyze the effectiveness factor for different values of Lewis number and heat of reaction (G factor). Lewis number includes the effect of thermal conductivity of the solid pellet. Higher the Lewis number, the higher will be the thermal conductivity of the solid. The effectiveness factor was found to be decreasing with decreasing Lewis number due to the fact that smaller Lewis numbers retard the rate of heat transfer inside the pellet owing to a lower rate of pyrolysis reaction. G factor includes the effect of the heat of reaction. Since the pyrolysis reaction is endothermic in nature, the G factor takes negative values. The more the negative value higher will be endothermic nature of the pyrolysis reaction. The effectiveness factor was found to be decreasing with more negative values of the G factor. This behavior can be attributed to the fact that more negative value of G factor would result in more energy consumption by the reaction owing to a larger temperature gradient inside the pellet. Further, the analytical expressions are also derived for gas and solid concentrations and effectiveness factor for two limiting cases of the general model developed. The two limiting cases of the model are categorized as the homogeneous model and unreacted shrinking core model.

Keywords: effectiveness factor, G-factor, homogeneous model, lewis number, non-catalytic, shrinking core model

Procedia PDF Downloads 126

Authors: masmoudi Jabri Khaoula, Zitouni Hana, Bousselmi Latifa, Akrout Hanen

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Mathematical modeling has become an essential tool for sustainable wastewater management, particularly for the simulation and the optimization of complex processes involved in activated sludge systems. In this context, the activated sludge model (ASM3h) was used for the simulation of a Biological Membrane Reactor (MBR) as it includes the integration of biological wastewater treatment and physical separation by membrane filtration. In this study, the MBR with a useful volume of 12.5 L was fed continuously with poultry slaughterhouse wastewater (PSWW) for 50 days at a feed rate of 2 L/h and for a hydraulic retention time (HRT) of 6.25h. Throughout its operation, High removal efficiency was observed for the removal of organic pollutants in terms of COD with 84% of efficiency. Moreover, the MBR has generated a treated effluent which fits with the limits of discharge into the public sewer according to the Tunisian standards which were set in March 2018. In fact, for the nitrogenous compounds, average concentrations of nitrate and nitrite in the permeat reached 0.26±0.3 mg. L-1 and 2.2±2.53 mg. L-1, respectively. The simulation of the MBR process was performed using SIMBA software v 5.0. The state variables employed in the steady state calibration of the ASM3h were determined using physical and respirometric methods. The model calibration was performed using experimental data obtained during the first 20 days of the MBR operation. Afterwards, kinetic parameters of the model were adjusted and the simulated values of COD, N-NH4+and N- NOx were compared with those reported from the experiment. A good prediction was observed for the COD, N-NH4+and N- NOx concentrations with 467 g COD/m³, 110.2 g N/m³, 3.2 g N/m³ compared to the experimental data which were 436.4 g COD/m³, 114.7 g N/m³ and 3 g N/m³, respectively. For the validation of the model under dynamic simulation, the results of the experiments obtained during the second treatment phase of 30 days were used. It was demonstrated that the model simulated the conditions accurately by yielding a similar pattern on the variation of the COD concentration. On the other hand, an underestimation of the N-NH4+ concentration was observed during the simulation compared to the experimental results and the measured N-NO3 concentrations were lower than the predicted ones, this difference could be explained by the fact that the ASM models were mainly designed for the simulation of biological processes in the activated sludge systems. In addition, more treatment time could be required by the autotrophic bacteria to achieve a complete and stable nitrification. Overall, this study demonstrated the effectiveness of mathematical modeling in the prediction of the performance of the MBR systems with respect to organic pollution, the model can be further improved for the simulation of nutrients removal for a longer treatment period.

Keywords: activated sludge model (ASM3h), membrane bioreactor (MBR), poultry slaughter wastewater (PSWW), reuse

Procedia PDF Downloads 51

Authors: Jean Pierre Ribeiro Daquila

Abstract:

This paper aims to analyze the use of the Theory of Multiple Intelligences as a tool to facilitate students’ learning. This theory, proposed by the American psychologist and educator Howard Gardner, was first established in 1983 and advocates that human beings possess eight intelligence and not only one, as defended by psychologists prior to his theory. These intelligence are bodily-kinesthetic intelligence, musical, linguistic, logical-mathematical, spatial, interpersonal, intrapersonal, and naturalist. This paper will focus on bodily-kinesthetic intelligence. Spatial and bodily-kinesthetic intelligences are sensed by athletes, dancers, and others who use their bodies in ways that exceed normal abilities. These are intelligences that are closely related. A quarterback or a ballet dancer needs to have both an awareness of body motions and abilities as well as a sense of the space involved in the action. Nevertheless, there are many reasons which make classical ballet dance more integrated with other intelligences. Ballet dancers make it look effortless as they move across the stage, from the lifts to the toe points; therefore, there is acting both in the performance of the repertoire and in hiding the pain or physical stress. The ballet dancer has to have great mathematical intelligence to perform a fast allegro; for instance, each movement has to be executed in a specific millisecond. Flamenco dancers need to rely as well on their mathematic abilities, as the footwork requires the ability to make half, two, three, four or even six movements in just one beat. However, the precision of the arm movements is freer than in ballet dance; for this reason, ballet dancers need to be more holistically aware of their movements; therefore, our experiment will test whether this greater attention required by ballet dancers makes them acquire better results in the training sessions when compared to flamenco dancers. An experiment will be carried out in this study by training ballet dancers through dance (four years of experience dancing minimum – experimental group 1); a group of flamenco dancers (four years of experience dancing minimum – experimental group 2). Both experimental groups will be trained in two different domains – phonetics and chemistry – to examine whether there is a significant improvement in these areas compared to the control group (a group of regular students who will receive the same training through a traditional method). However, this paper will focus on phonetic training. Experimental group 1 will be trained with the aid of classical music plus bodily work. Experimental group 2 will be trained with flamenco rhythm and kinesthetic work. We would like to highlight that this study takes dance as an example of a possible area of strength; nonetheless, other types of arts can and should be used to support students, such as drama, creative writing, music and others. The main aim of this work is to suggest that other intelligences, in the case of this study, bodily-kinesthetic, can be used to help improve pronunciation.

Keywords: multiple intelligences, pronunciation, effective pronunciation trainings, short drills, musical intelligence, bodily-kinesthetic intelligence

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Authors: Sylwia Krawiec, Joanna Cabaj, Karol Malecha

Abstract:

Nowadays, progress in the field of the analytical methods is of great interest for reliable biological research and medical diagnostics. Classical techniques of chemical analysis, despite many advantages, do not permit to obtain immediate results or automatization of measurements. Chemical sensors have displaced the conventional analytical methods - sensors combine precision, sensitivity, fast response and the possibility of continuous-monitoring. Biosensor is a chemical sensor, which except of conventer also possess a biologically active material, which is the basis for the detection of specific chemicals in the sample. Each biosensor device mainly consists of two elements: a sensitive element, where is recognition of receptor-analyte, and a transducer element which receives the signal and converts it into a measurable signal. Through these two elements biosensors can be divided in two categories: due to the recognition element (e.g immunosensor) and due to the transducer (e.g optical sensor). Working of optical sensor is based on measurements of quantitative changes of parameters characterizing light radiation. The most often analyzed parameters include: amplitude (intensity), frequency or polarization. Changes in the optical properties one of the compound which reacts with biological material coated on the sensor is analyzed by a direct method, in an indirect method indicators are used, which changes the optical properties due to the transformation of the testing species. The most commonly used dyes in this method are: small molecules with an aromatic ring, like rhodamine, fluorescent proteins, for example green fluorescent protein (GFP), or nanoparticles such as quantum dots (QDs). Quantum dots have, in comparison with organic dyes, much better photoluminescent properties, better bioavailability and chemical inertness. These are semiconductor nanocrystals size of 2-10 nm. This very limited number of atoms and the ‘nano’-size gives QDs these highly fluorescent properties. Rapid and sensitive detection of dopamine is extremely important in modern medicine. Dopamine is very important neurotransmitter, which mainly occurs in the brain and central nervous system of mammals. Dopamine is responsible for the transmission information of moving through the nervous system and plays an important role in processes of learning or memory. Detection of dopamine is significant for diseases associated with the central nervous system such as Parkinson or schizophrenia. In developed optical biosensor for detection of dopamine, are used graphene quantum dots (GQDs). In such sensor dopamine molecules coats the GQD surface - in result occurs quenching of fluorescence due to Resonance Energy Transfer (FRET). Changes in fluorescence correspond to specific concentrations of the neurotransmitter in tested sample, so it is possible to accurately determine the concentration of dopamine in the sample.

Keywords: biosensor, dopamine, fluorescence, quantum dots

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Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

Abstract:

Large eddy simulation (LES) has been extensively used in the investigation of turbulence. LES calculates the grid-resolved large-scale motions and leaves small scales modeled by sublfilterscale (SFS) models. Among the existing SFS models, the deconvolution model has been used successfully in the LES of the engineering flows and geophysical flows. Despite the wide application of deconvolution models, the effects of subfilter scale dynamics and filter anisotropy on the accuracy of SFS modeling have not been investigated in depth. The results of LES are highly sensitive to the selection of filters and the anisotropy of the grid, which has been overlooked in previous research. In the current study, two critical aspects of LES are investigated. Firstly, we analyze the influence of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) at varying filter-to-grid ratios (FGR) in isotropic turbulence. An array of invertible filters are employed, encompassing Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The significance of FGR becomes evident, as it acts as a pivotal factor in error control for precise SFS stress prediction. When FGR is set to 1, the DDM models cannot accurately reconstruct the SFS stress due to the insufficient resolution of SFS dynamics. Notably, prediction capabilities are enhanced at an FGR of 2, resulting in accurate SFS stress reconstruction, except for cases involving Helmholtz I and II filters. A remarkable precision close to 100% is achieved at an FGR of 4 for all DDM models. Additionally, the further exploration extends to the filter anisotropy to address its impact on the SFS dynamics and LES accuracy. By employing dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with the anisotropic filter, aspect ratios (AR) ranging from 1 to 16 in LES filters are evaluated. The findings highlight the DDM's proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. High correlation coefficients exceeding 90% are observed in the a priori study for the DDM's reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as lter anisotropy increases. In the a posteriori studies, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, encompassing velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strain-rate tensors, and SFS stress. It is observed that as filter anisotropy intensify, the results of DSM and DMM become worse, while the DDM continues to deliver satisfactory results across all filter-anisotropy scenarios. The findings emphasize the DDM framework's potential as a valuable tool for advancing the development of sophisticated SFS models for LES of turbulence.

Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence

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