Search results for: neural simulated annealing

Authors: Paolo Sassi, Jorge Freiria, Gabriel Usera

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In this work, a finite volume fluid flow solver is coupled with a discrete element method module for the simulation of the dynamics of free and elastic bodies in interaction with the fluid and between themselves. The open source fluid flow solver, caffa3d.MBRi, includes the capability to work with nested overlapping grids in order to easily refine the grid in the region where the bodies are moving. To do so, it is necessary to implement a recognition function able to identify the specific mesh block in which the device is moving in. The set of overlapping finer grids might be displaced along with the set of bodies being simulated. The interaction between the bodies and the fluid is computed through a two-way coupling. The velocity field of the fluid is first interpolated to determine the drag force on each object. After solving the objects displacements, subject to the elastic bonding among them, the force is applied back onto the fluid through a Gaussian smoothing considering the cells near the position of each object. The fishnet is represented as lumped masses connected by elastic lines. The internal forces are derived from the elasticity of these lines, and the external forces are due to drag, gravity, buoyancy and the load acting on each element of the system. When solving the ordinary differential equations system, that represents the motion of the elastic and flexible bodies, it was found that the Runge Kutta solver of fourth order is the best tool in terms of performance, but requires a finer grid than the fluid solver to make the system converge, which demands greater computing power. The coupled solver is demonstrated by simulating the interaction between the fluid, an elastic fishnet and a set of free bodies being captured by the net as they are dragged by the fluid. The deformation of the net, as well as the wake produced in the fluid stream are well captured by the method, without requiring the fluid solver mesh to adapt for the evolving geometry. Application of the same strategy to the simulation of elastic structures subject to the action of wind is also possible with the method presented, and one such application is currently under development.

Keywords: computational fluid dynamics, discrete element method, fishnets, nested overlapping grids

Procedia PDF Downloads 410

Authors: Aschalew C. Workneh, K. S. Hari Prasad, C. S. P. Ojha

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The crop water stress index (CWSI) is a cost-effective, non-destructive, and simple technique for tracking the start of crop water stress. This study investigated the feasibility of CWSI derived from canopy temperature to detect the water status of wheat crops. Artificial intelligence (AI) techniques have become increasingly popular in recent years for determining CWSI. In this study, the performance of two AI techniques, adaptive neuro-fuzzy inference system (ANFIS) and self-organizing maps (SOM), are compared while determining the CWSI of paddy crops. Field experiments were conducted for varying irrigation water applications during two seasons in 2022 and 2023 at the irrigation field laboratory at the Civil Engineering Department, Indian Institute of Technology Roorkee, India. The ANFIS and SOM-simulated CWSI values were compared with the experimentally calculated CWSI (EP-CWSI). Multiple regression analysis was used to determine the upper and lower CWSI baselines. The upper CWSI baseline was found to be a function of crop height and wind speed, while the lower CWSI baseline was a function of crop height, air vapor pressure deficit, and wind speed. The performance of ANFIS and SOM were compared based on mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE), index of agreement (d), Nash-Sutcliffe efficiency (NSE), and coefficient of correlation (R²). Both models successfully estimated the CWSI of the paddy crop with higher correlation coefficients and lower statistical errors. However, the ANFIS (R²=0.81, NSE=0.73, d=0.94, RMSE=0.04, MAE= 0.00-1.76 and MBE=-2.13-1.32) outperformed the SOM model (R²=0.77, NSE=0.68, d=0.90, RMSE=0.05, MAE= 0.00-2.13 and MBE=-2.29-1.45). Overall, the results suggest that ANFIS is a reliable tool for accurately determining CWSI in wheat crops compared to SOM.

Keywords: adaptive neuro-fuzzy inference system, canopy temperature, crop water stress index, self-organizing map, wheat

Procedia PDF Downloads 42

Authors: Hendri Purwadi, Christine McCloud

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Triage is a complex decision-making process that aims to categorize a victim’s level of acuity and the need for medical assistance. Two common triage systems have been widely used in Mass Casualty Incidents (MCIs) and disaster situation are START (Simple triage algorithm and rapid treatment) and SALT (sort, asses, lifesaving, intervention, and treatment/transport). There is currently controversy regarding the effectiveness of SALT over START triage system. This systematic review aims to investigate and compare the effectiveness between SALT and START triage system in disaster and MCIs setting. Literatures were searched via systematic search strategy from 2009 until 2019 in PubMed, Cochrane Library, CINAHL, Scopus, Science direct, Medlib, ProQuest. This review included simulated-based and medical record -based studies investigating the accuracy and applicability of SALT and START triage systems of adult and children population during MCIs and disaster. All type of studies were included. Joana Briggs institute critical appraisal tools were used to assess the quality of reviewed studies. As a result, 1450 articles identified in the search, 10 articles were included. Four themes were identified by review, they were accuracy, under-triage, over-triage and time to triage per individual victim. The START triage system has a wide range and inconsistent level of accuracy compared to SALT triage system (44% to 94. 2% of START compared to 70% to 83% of SALT). The under-triage error of START triage system ranged from 2.73% to 20%, slightly lower than SALT triage system (7.6 to 23.3%). The over-triage error of START triage system was slightly greater than SALT triage system (START ranged from 2% to 53% compared to 2% to 22% of SALT). The time for applying START triage system was faster than SALT triage system (START was 70-72.18 seconds compared to 78 second of SALT). Consequently; The START triage system has lower level of under-triage error and faster than SALT triage system in classifying victims of MCIs and disaster whereas SALT triage system is known slightly more accurate and lower level of over-triage. However, the magnitude of these differences is relatively small, and therefore the effect on the patient outcomes is not significance. Hence, regardless of the triage error, either START or SALT triage system is equally effective to triage victims of disaster and MCIs.

Keywords: disaster, effectiveness, mass casualty incidents, START triage system, SALT triage system

Procedia PDF Downloads 126

Authors: Eugene Y. C. Wong, Daniel Y. W. Mo, Cosmo T. Y. Ng, Jessica K. Y. Chan, Leith K. Y. Chan, Henry Y. K. Lau

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The real-time planning visualisation, precise allocation and loading optimisation in air cargo load planning operations are increasingly important as more considerations are needed on dangerous cargo loading, locations of lithium batteries, weight declaration and limited aircraft capacity. The planning of the unit load devices (ULD) can often be carried out only in a limited number of hours before flight departure. A dynamic air cargo load planning system is proposed with the optimisation of cargo load plan and visualisation of planning results in virtual reality systems. The system aims to optimise the cargo load planning and visualise the simulated loading planning decision on air cargo terminal operations. Adopting simulation tools, Cave Automatic Virtual Environment (CAVE) and virtual reality technologies, the results of planning with reference to weight and balance, Unit Load Device (ULD) dimensions, gateway, cargo nature and aircraft capacity are optimised and presented. The virtual reality system facilities planning, operations, education and training. Staff in terminals are usually trained in a traditional push-approach demonstration with enormous manual paperwork. With the support of newly customized immersive visualization environment, users can master the complex air cargo load planning techniques in a problem based training with the instant result being immersively visualised. The virtual reality system is developed with three-dimensional (3D) projectors, screens, workstations, truss system, 3D glasses, and demonstration platform and software. The content will be focused on the cargo planning and loading operations in an air cargo terminal. The system can assist decision-making process during cargo load planning in the complex operations of air cargo terminal operations. The processes of cargo loading, cargo build-up, security screening, and system monitoring can be further visualised. Scenarios are designed to support and demonstrate the daily operations of the air cargo terminal, including dangerous goods, pets and animals, and some special cargos.

Keywords: air cargo load planning, optimisation, virtual reality, weight and balance, unit load device

Procedia PDF Downloads 339

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 110

Authors: Zhichao Song, Swanand Vaidya

Abstract:

Nowadays, most portable (e.g., smartphones) and wearable (e.g., smartwatches and earphones) consumer hardware are designed to be dustproof following IP5 or IP6 ratings to ensure the product is able to handle potentially dusty outdoor environments. On the other hand, the design guideline is relatively vague for indoor devices (e.g., smart displays and speakers). While it is generally believed that the indoor environment is much less dusty, in certain circumstances, dust ingression is still able to cause functional failures, such as microphone frequency response shift and camera black spot, or cosmetic dissatisfaction, mainly the dust build up in visible pockets and gaps which is hard to clean. In this paper, we developed a simulation methodology to analyze dust settlement and ingression into known ports of a device. A closed system is initialized with dust particles whose sizes follow Weibull distribution based on data collected in a user study, and dust particle movement was approximated as a settlement in stationary fluid, which is governed by Stokes’ law. Following this method, we simulated dust ingression into MEMS microphone through the acoustic port and protective mesh. Various design and environmental parameters are evaluated including mesh pore size, acoustic port depth-to-diameter ratio, mass density of dust material and inclined angle of microphone port. Although the dependencies of dust resistance on these parameters are all monotonic, smaller mesh pore size, larger acoustic depth-to-opening ratio and more inclined microphone placement (towards horizontal direction) are preferred for dust resistance; these preferences may represent certain trade-offs in audio performance and compromise in industrial design. The simulation results suggest the quantitative ranges of these parameters, with more pronounced effects in the improvement of dust resistance. Based on the simulation results, we proposed several design guidelines that intend to achieve an overall balanced design from audio performance, dust resistance, and flexibility in industrial design.

Keywords: dust settlement, numerical simulation, microphone design, Weibull distribution, Stoke's equation

Procedia PDF Downloads 100

Authors: C. Yin, B. Zhang, Y. Liu, J. Cai

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Airborne EM (AEM) is an effective geophysical exploration tool, especially suitable for ridged mountain areas. In these areas, topography will have serious effects on AEM system responses. However, until now little study has been reported on topographic effect on airborne EM systems. In this paper, an edge-based unstructured finite-element (FE) method is developed for 3D topographic modeling for both frequency and time-domain airborne EM systems. Starting from the frequency-domain Maxwell equations, a vector Helmholtz equation is derived to obtain a stable and accurate solution. Considering that the AEM transmitter and receiver are both located in the air, the scattered field method is used in our modeling. The Galerkin method is applied to discretize the Helmholtz equation for the final FE equations. Solving the FE equations, the frequency-domain AEM responses are obtained. To accelerate the calculation speed, the response of source in free-space is used as the primary field and the PARDISO direct solver is used to deal with the problem with multiple transmitting sources. After calculating the frequency-domain AEM responses, a Hankel’s transform is applied to obtain the time-domain AEM responses. To check the accuracy of present algorithm and to analyze the characteristic of topographic effect on airborne EM systems, both the frequency- and time-domain AEM responses for 3 model groups are simulated: 1) a flat half-space model that has a semi-analytical solution of EM response; 2) a valley or hill earth model; 3) a valley or hill earth with an abnormal body embedded. Numerical experiments show that close to the node points of the topography, AEM responses demonstrate sharp changes. Special attentions need to be paid to the topographic effects when interpreting AEM survey data over rugged topographic areas. Besides, the profile of the AEM responses presents a mirror relation with the topographic earth surface. In comparison to the topographic effect that mainly occurs at the high-frequency end and early time channels, the EM responses of underground conductors mainly occur at low frequencies and later time channels. For the signal of the same time channel, the dB/dt field reflects the change of conductivity better than the B-field. The research of this paper will serve airborne EM in the identification and correction of the topographic effects.

Keywords: 3D, Airborne EM, forward modeling, topographic effect

Procedia PDF Downloads 306

Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

Procedia PDF Downloads 155

Authors: Zhiqiang Yan, Chen Fan, Yafei Wang, Beicheng Xia

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Urban lakes play an invaluable role in urban water systems such as flood control, water supply, and public recreation. However, over 38% of the urban lakes have suffered from severe eutrophication in China. Chlorine that could remarkably inhibit the growth of phytoplankton in eutrophic, has been widely used in the agricultural, aquaculture and industry in the recent past. However, little information has been reported regarding the effects of chlorine on the lake ecosystem, especially on the main aquatic species.To investigate the ecological response of main aquatic species and system stability to chlorine interference in shallow urban lakes, a mini system dynamic model was developed based on the competition and predation of main aquatic species and total phosphorus circulation. The main species of submerged macrophyte, phytoplankton, zooplankton, benthos, spiroggra and total phosphorus in water and sediment were used as variables in the model,while the interference of chlorine on phytoplankton was represented by an exponential attenuation equation. Furthermore, the eco-exergy expressing the development degree of ecosystem was used to quantify the complexity of the shallow urban lake. The model was validated using the data collected in the Lotus Lake in Guangzhoufrom1 October 2015 to 31 January 2016.The correlation coefficient (R), root mean square error-observations standard deviation ratio (RSR) and index of agreement (IOA) were calculated to evaluate accuracy and reliability of the model.The simulated values showed good qualitative agreement with the measured values of all components. The model results showed that chlorine had a notable inhibitory effect on Microcystis aeruginos，Rachionus plicatilis, Diaphanosoma brachyurum Liévin and Mesocyclops leuckarti (Claus).The outbreak of Spiroggra.spp. inhibited the growth of Vallisneria natans (Lour.) Hara, leading to a gradual decrease of eco-exergy and the breakdown of ecosystem internal equilibria. This study gives important insight into using chlorine to achieve eutrophication control and understand mechanism process.

Keywords: system dynamic model, urban lake, chlorine, eco-exergy

Procedia PDF Downloads 225

Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

Procedia PDF Downloads 239

Authors: Rahman Davtalab

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Florida is one of the most vulnerable states to natural disasters among the 50 states of the USA. The state exposed by tropical storms, hurricanes, storm surge, landslide, etc. Besides, the mentioned natural phenomena, global warming, sea-level rise, and other anthropogenic environmental changes make a very complicated and unpredictable system for decision-makers. In this study, we tried to highlight the effects of climate change and sea-level rise on surface water and groundwater systems for three different geographical locations in Florida; Main Canal of Jacksonville Beach (in the northeast of Florida adjacent to the Atlantic Ocean), Grace Lake in central Florida, far away from surrounded coastal line, and Mc Dill in Florida and adjacent to Tampa Bay and Mexican Gulf. An integrated hydrologic and hydraulic model was developed and simulated for all three cases, including surface water, groundwater, or a combination of both. For the case study of Main Canal-Jacksonville Beach, the investigation showed that a 76 cm sea-level rise in time horizon 2060 could increase the flow velocity of the tide cycle for the main canal's outlet and headwater. This case also revealed how the sea level rise could change the tide duration, potentially affecting the coastal ecosystem. As expected, sea-level rise can raise the groundwater level. Therefore, for the Mc Dill case, the effect of groundwater rise on soil storage and the performance of stormwater retention ponds is investigated. The study showed that sea-level rise increased the pond’s seasonal high water up to 40 cm by time horizon 2060. The reliability of the retention pond is dropped from 99% for the current condition to 54% for the future. The results also proved that the retention pond could not retain and infiltrate the designed treatment volume within 72 hours, which is a significant indication of increasing pollutants in the future. Grace Lake case study investigates the effects of climate change on groundwater recharge. This study showed that using the dynamically downscaled data of the groundwater recharge can decline up to 24% by the mid-21st century.

Keywords: groundwater, surface water, Florida, retention pond, tide, sea level rise

Procedia PDF Downloads 175

Authors: Nafiseh Mirabdolhosseini, Jerry Zhou, Vincent Ho

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The dramatic global rise in acid reflux has also led to oesophageal adenocarcinoma (OAC) becoming the fastest-growing cancer in developed countries. While gastroscopy with biopsy is used to diagnose OAC patients, this labour-intensive and expensive process is not suitable for population screening. This study aims to design, develop, and implement a minimally invasive system to capture optical data of the distal oesophagus for rapid screening of potential abnormalities. To develop the system and understand user requirements, a user-centric approach was employed by utilising co-design strategies. Target users’ segments were identified, and 38 patients and 14 health providers were interviewed. Next, the technical requirements were developed based on consultations with the industry. A minimally invasive optical system was designed and developed considering patient comfort. This system consists of the sensing catheter, controller unit, and analysis program. Its procedure only takes 10 minutes to perform and does not require cleaning afterward since it has a single-use catheter. A prototype system was evaluated for safety and efficacy for both laboratory and clinical performance. This prototype performed successfully when submerged in simulated gastric fluid without showing evidence of erosion after 24 hours. The system effectively recorded a video of the mid-distal oesophagus of a healthy volunteer (34-year-old male). The recorded images were used to develop an automated program to identify abnormalities in the distal oesophagus. Further data from a larger clinical study will be used to train the automated program. This system allows for quick visual assessment of the lower oesophagus in primary care settings and can serve as a screening tool for oesophageal adenocarcinoma. In addition, this system is able to be coupled with 24hr ambulatory pH monitoring to better correlate oesophageal physiological changes with reflux symptoms. It also can provide additional information on lower oesophageal sphincter functions such as opening times and bolus retention.

Keywords: endoscopy, MedTech, oesophageal adenocarcinoma, optical system, screening tool

Procedia PDF Downloads 76

Authors: Christina Peristeri, Tobias Berg, Domenico Caridi, Paul Hutcheson, Robert Winstanley

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In recent years the demand for rapid innovations in the automotive industry has led to the need for accelerated simulation procedures while retaining a detailed representation of the simulated phenomena. The project’s aim is to create a fast transient workflow for external aerodynamic CFD simulations of road vehicles. The geometry used was the SAE Notchback Closed Cooling DrivAer model, and the simulation results were compared with data from wind tunnel tests. The meshes generated for this study were of two types. One was a mix of polyhedral cells near the surface and hexahedral cells away from the surface. The other was an octree hex mesh with a rapid method of fitting to the surface. Three different grid refinement levels were used for each mesh type, with the biggest total cell count for the octree mesh being close to 1 billion. A series of steady-state solutions were obtained on three different grid levels using a pseudo-transient coupled solver and a k-omega-based RANS turbulence model. A mesh-independent solution was found in all cases with a medium level of refinement with 200 million cells. Stress-Blended Eddy Simulation (SBES) was chosen for the transient simulations, which uses a shielding function to explicitly switch between RANS and LES mode. A converged pseudo-transient steady-state solution was used to initialize the transient SBES run that was set up with the SIMPLEC pressure-velocity coupling scheme to reach the fastest solution (on both CPU & GPU solvers). An important part of this project was the use of FLUENT’s Multi-GPU solver. Tesla A100 GPU has been shown to be 8x faster than an Intel 48-core Sky Lake CPU system, leading to significant simulation speed-up compared to the traditional CPU solver. The current study used 4 Tesla A100 GPUs and 192 CPU cores. The combination of rapid octree meshing and GPU computing shows significant promise in reducing time and hardware costs for industrial strength aerodynamic simulations.

Keywords: CFD, DrivAer, LES, Multi-GPU solver, octree mesh, RANS

Procedia PDF Downloads 108

Authors: V. S. Manivasagam, R. Nagarajan

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Modeling the soil, water and crop growth interactions are attaining major importance, considering the future climate change and water availability for agriculture to meet the growing food demand. Progress in understanding the crop growth response during water stress period through crop modeling approach provides an opportunity for improving and sustaining the future agriculture water use efficiency. An attempt has been made to evaluate the potential use of crop modeling approach for assessing the minimal supplementary irrigation requirement for crop growth during water limited condition and its practical significance in sustainable improvement of crop yield and water productivity. Among the numerous crop models, water driven-AquaCrop model has been chosen for the present study considering the modeling approach and water stress impact on yield simulation. The study has been evaluated in rainfed maize grown area of semi-arid Shanmuganadi watershed (a tributary of the Cauvery river system) located in southern India during the rabi cropping season (October-February). In addition to actual rainfed maize growth simulation, irrigated maize scenarios were simulated for assessing the supplementary irrigation requirement during water shortage condition for the period 2012-2015. The simulation results for rainfed maize have shown that the average maize yield of 0.5-2 t ha-1 was observed during deficit monsoon season (<350 mm) whereas 5.3 t ha-1 was noticed during sufficient monsoonal period (>350 mm). Scenario results for irrigated maize simulation during deficit monsoonal period has revealed that 150-200 mm of supplementary irrigation has ensured the 5.8 t ha-1 of irrigated maize yield. Thus, study results clearly portrayed that minimal application of supplementary irrigation during the critical growth period along with the deficit rainfall has increased the crop water productivity from 1.07 to 2.59 kg m-3 for major soil types. Overall, AquaCrop is found to be very effective for the sustainable irrigation assessment considering the model simplicity and minimal inputs requirement.

Keywords: AquaCrop, crop modeling, rainfed maize, water stress

Procedia PDF Downloads 257

Authors: Kara J. Coffman-Rea, Karen E. Samonds

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Microplastic pollution is an urgent global threat to our planet and human health. Microplastic particles have been detected within our food, water, and atmosphere, and found within the human stool, placenta, and lung tissue. However, most spectrometric microplastic detection methods require chemical digestion which can alter or destroy microplastic particles and makes it impossible to acquire information about their in-situ distribution. MALDI TOF MS (Matrix-assisted laser desorption ionization-time of flight mass spectrometry) is an analytical method using a soft ionization technique that can be used for polymer analysis. This method provides a valuable opportunity to both acquire information regarding the in-situ distribution of microplastics and also minimizes the destructive element of chemical digestion. In addition, MALDI TOF MS allows for expanded analysis of the microplastics including detection of specific additives that may be present within them. MALDI TOF MS is particularly sensitive to sample preparation and has not yet been used to analyze environmental microplastics within their specific location (e.g., biological tissues, sediment, water). In this study, microplastics were created using polyethylene gloves, polystyrene micro-foam, and polyethylene terephthalate cable sleeving. Plastics were frozen using liquid nitrogen and ground to obtain small fragments. An artificial tissue was created using a cellulose sponge as scaffolding coated with a MaxGel Extracellular Matrix to simulate human lung tissue. Optimal preparation techniques (e.g., matrix, cationization reagent, solvent, mixing ratio, laser intensity) were first established for each specific polymer type. The artificial tissue sample was subsequently spiked with microplastics, and specific polymers were detected using MALDI-TOF-MS. This study presents a novel method for the detection of environmental polyethylene, polyethylene terephthalate, and polystyrene microplastics within a complex sample. Results of this study provide an effective method that can be used in future microplastics research and can aid in determining the potential threats to environmental and human health that they pose.

Keywords: environmental plastic pollution, MALDI-TOF MS, microplastics, polymer identification

Procedia PDF Downloads 243

Authors: Reihane Mehrparvar

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Human age could exceed further by altering gene expression through food intaking, although as a consequence of recent century eating patterns, human life-span getting shorter by emerging irregulating in autophagy mechanism, insulin, leptin, gut microbiota which are important etiological factors of type-2 diabetes, obesity, infertility, cancer, metabolic and autoimmune diseases. However, restricted calorie intake and vigorous exercise might be beneficial for losing weight and metabolic regulation in a short period but could not be implementable in the long term as a way of life. Therefore, the lack of a dietary program that is compatible with the genes of the body is essential. Sweet and high-glycemic-index (HGI) foods were associated with type-2 diabetes and cancer morbidity. The neuropsychological perspective characterizes the inclination of sweet and HGI-food consumption as addictive behavior; hence this process engages preference of gut microbiota, neural node, and dopaminergic functions. Moreover, meal composition is not the only factor that affects body hemostasis. In this narrative review, it is believed to attempt to investigate how the body responded to different food intakes and represent an accurate model based on current evidence. Eating frequently and ingesting unassimilable protein and carbohydrates may not be compatible with human genes and could cause impairments in the self-renovation mechanism. This trajectory indicates our body is more adapted to starvation and eating animal meat and marrow. Here has been recommended a model that takes into account three important factors: frequent eating, meal composition, and circadian rhythm, which may offer a promising intervention for obesity, inflammation, cardiovascular, autoimmune disorder, type-2 diabetes, insulin resistance, infertility, and cancer through intensifying autophagy-mechanism and eliminate medical costs.

Keywords: metabolic disease, anti-aging, type-2 diabetes, autophagy

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Authors: Eronini Iheanyi Umez-Eronini

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Most studies and existing implementations of compressed air energy storage (CAES) coupled with a wind farm to overcome intermittency and variability of wind power are based on bulk or centralized CAES plants. A dynamic model of a hybrid wind farm with wind turbines and distributed CAES, consisting of air storage tanks and compressor and expander trains at each wind turbine station, is developed and simulated in MATLAB. An ad hoc supervisory controller, in which the wind turbines are simply operated under classical power optimizing region control while scheduling power production by the expanders and air storage by the compressors, including modulation of the compressor power levels within a control range, is used to regulate overall farm power production to track minute-scale (3-minutes sampling period) TSO absolute power reference signal, over an eight-hour period. Simulation results for real wind data input with a simple wake field model applied to a hybrid plant composed of ten 5-MW wind turbines in a row and ten compatibly sized and configured Diabatic CAES stations show the plant controller is able to track the power demand signal within an error band size on the order of the electrical power rating of a single expander. This performance suggests that much improved results should be anticipated when the global D-CAES control is combined with power regulation for the individual wind turbines using available approaches for wind farm active power control. For standalone power plant fuel electrical efficiency estimate of up to 60%, the round trip electrical storage efficiency computed for the distributed CAES wherein heat generated by running compressors is utilized in the preheat stage of running high pressure expanders while fuel is introduced and combusted before the low pressure expanders, was comparable to reported round trip storage electrical efficiencies for bulk Adiabatic CAES.

Keywords: hybrid wind farm, distributed CAES, diabatic CAES, active power control, dynamic modeling and simulation

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Authors: Akbar Haghzadeh Saraskanroud, Amir Hossein Yahyavi Zanjani, Niloofar Kargar, Hanieh Ahrabi

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Cervical cancer refers to an unusual growth of cells in the cervix. The cervix is the lower part of the uterus, which connects to the vagina. Various risk factors such as human papillomavirus (HPV), having a weakened immune system, smoking or breathing in secondhand smoke, reproductive factors, and obesity play important roles in causing most cervical cancers. When cervical cancer happens, surgery is often the first treatment option to remove it. Other treatments might include chemotherapy and targeted therapy medicines. Radiation therapy with high-energy photon beams also may be used. Sometimes combined treatment, including radiation with low-dose chemotherapy, was applied. Intracavitary brachytherapy is an integral part of radiotherapy for locally advanced gynecologic malignancies such as cervical cancer. In the treatment of cervical cancer, there are different tools for doing brachytherapy. Two combinations of different applicators for this purpose are Tandem and Ovoid and Tandem and Ring. This study evaluated the dose differences between these two methods in the organs at risk of the rectum, sigmoid, and bladder. In this study, the treatment planswere simulated by the Oncentra treatment planning system and Tandem, Ovid, and Rings of different sizes. CT scan images of 23 patients were treated with HDR_BT Elekta Flexitron system were used for this study. Contouring of HR-CTV, rectum and bladder was performed for all patients. Then, the received dose of 0.1 and 0.2cc volumes of organs at risk were obtained and compared for these two methods: T-Ovoid and T-Ring. By doing investigations and dose measurements of points A and B and the volumes specified by ICRU, it seems that when comparing ring and ovoid to tandem and ovoid, the total dose to the rectum was lower by about 11%, and the bladder was 7%. In the case of HR CTV, this comparison showed that this ratio is about 7% better. Figure 1 shows the amount of decrease in rectum dose in the T-Ring method compared to T-Ovoid. Figure 2 indicates the amount of decrease in bladder dose in the T-Ring method compared to T-Ovoid. Finally, figure 3 illustrates the amount of HR-CTV coverage in the T-Ring method compared to the T-Ovoid.

Keywords: cervical cancer, brachytherapy, rectum, tandem and ovoid, tandem and ring.

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Authors: Fatemeh Jannesarvatan, Ghazaal Parastooei, Jimmy frerejan, Saedeh Mokhtari, Peter Van Rosmalen

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Medical and dental education increasingly emphasizes the acquisition, integration, and coordination of complex knowledge, skills, and attitudes that can be applied in practical situations. Instructional design approaches have focused on using real-life tasks in order to facilitate complex learning in both real and simulated environments. The Four component instructional design (4C/ID) model has become a useful guideline for designing instructional materials that improve learning transfer, especially in health profession education. The objective of this study was to apply the 4C/ID model in the creation of virtual patients (VPs) that dental students can use to practice their clinical management and clinical reasoning skills. The study first explored the context and concept of complication factors and common errors for novices and how they can affect the design of a virtual patient program. The study then selected key dental information and considered the content needs of dental students. The design of virtual patients was based on the 4C/ID model's fundamental principles, which included: Designing learning tasks that reflect real patient scenarios and applying different levels of task complexity to challenge students to apply their knowledge and skills in different contexts. Creating varied learning materials that support students during the VP program and are closely integrated with the learning tasks and students' curricula. Cognitive feedback was provided at different levels of the program. Providing procedural information where students followed a step-by-step process from history taking to writing a comprehensive treatment plan. Four virtual patients were designed using the 4C/ID model's principles, and an experimental design was used to test the effectiveness of the principles in achieving the intended educational outcomes. The 4C/ID model provides an effective framework for designing engaging and successful virtual patients that support the transfer of knowledge and skills for dental students. However, there are some challenges and pitfalls that instructional designers should take into account when developing these educational tools.

Keywords: 4C/ID model, virtual patients, education, dental, instructional design

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Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Jeffrey Chase

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Historically the profession of Occupational Therapy was closely tied to the treatment of those suffering from mental illness; more recently, and especially in the U.S., the percentage of OTs identifying as working in the mental health area has declined significantly despite the estimate that by 2020 behavioral health disorders will surpass physical illnesses as the major cause of disability worldwide. In the U.S. less than 10% of OTs identify themselves as working with the mentally ill and/or practicing in mental health settings. Such a decline has implications for both those suffering from mental illness and the profession of Occupational Therapy. One reason cited for the decline of OT in mental health has been the limited research in the discipline addressing mental health practice. Despite significant advances in technology and growth in the field of neuroscience, major institutions and funding sources such as the National Institute of Mental Health (NIMH) have noted that research into the etiology and treatment of mental illness have met with limited success over the past 25 years. One major reason posited by NIMH is that research has been limited by how we classify individuals, that being mostly on what is observable. A new classification system being developed by NIMH, the Research Domain Criteria (RDoc), has the goal to look beyond just descriptors of disorders for common neural, genetic, and physiological characteristics that cut across multiple supposedly separate disorders. The hope is that by classifying individuals along RDoC measures that both reliability and validity will improve resulting in greater advances in the field. As a result of this change NIH and NIMH will prioritize research funding to those projects using the RDoC model. Multiple disciplines across many different setting will be required for RDoC or similar classification systems to be developed. During this shift in research methodology OT has an opportunity to reassert itself into the research and treatment of mental illness, both in developing new ways to more validly classify individuals, and to document the legitimacy of previously ill-defined and validated disorders such as sensory integration.

Keywords: global mental health and neuroscience, research opportunities for ot, greater integration of ot in mental health research, research and funding opportunities, research domain criteria (rdoc)

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Authors: Fikru Fentaw Abera

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Climate changes alter regional hydrologic conditions and results in a variety of impacts on water resource systems. Such hydrologic changes will affect almost every aspect of human well-being. The goal of this paper is to assess the impact of climate change on the hydrology of Upper Guder catchment located in northwest of Ethiopia. The GCM derived scenarios (HadCM3 A2a & B2a SRES emission scenarios) experiments were used for the climate projection. The statistical downscaling model (SDSM) was used to generate future possible local meteorological variables in the study area. The down-scaled data were then used as input to the soil and water assessment tool (SWAT) model to simulate the corresponding future stream flow regime in Upper Guder catchment of the Abay River Basin. A semi distributed hydrological model, SWAT was developed and Generalized Likelihood Uncertainty Estimation (GLUE) was utilized for uncertainty analysis. GLUE is linked with SWAT in the Calibration and Uncertainty Program known as SWAT-CUP. Three benchmark periods simulated for this study were 2020s, 2050s and 2080s. The time series generated by GCM of HadCM3 A2a and B2a and Statistical Downscaling Model (SDSM) indicate a significant increasing trend in maximum and minimum temperature values and a slight increasing trend in precipitation for both A2a and B2a emission scenarios in both Gedo and Tikur Inch stations for all three bench mark periods. The hydrologic impact analysis made with the downscaled temperature and precipitation time series as input to the hydrological model SWAT suggested for both A2a and B2a emission scenarios. The model output shows that there may be an annual increase in flow volume up to 35% for both emission scenarios in three benchmark periods in the future. All seasons show an increase in flow volume for both A2a and B2a emission scenarios for all time horizons. Potential evapotranspiration in the catchment also will increase annually on average 3-15% for the 2020s and 7-25% for the 2050s and 2080s for both A2a and B2a emissions scenarios.

Keywords: climate change, Guder sub-basin, GCM, SDSM, SWAT, SWAT-CUP, GLUE

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Authors: M. Belén Prendes-Gero, Celestino González-Nicieza, M. Inmaculada Alvarez-Fernández

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Roof collapse is one of the problems with a higher frequency in most of the mines of all countries, even now. There are many reasons that may cause the roof to collapse, namely the mine stress activities in the mining process, the lack of vigilance and carelessness or the complexity of the geological structure and irregular operations. This work is the result of the analysis of one accident produced in the “Mary” coal exploitation located in northern Spain. In this accident, the roof of a crossroad of excavated galleries to exploit the “Morena” Layer, 700 m deep, collapsed. In the paper, the work done by the forensic team to determine the causes of the incident, its conclusions and recommendations are collected. Initially, the available documentation (geology, geotechnics, mining, etc.) and accident area were reviewed. After that, laboratory and on-site tests were carried out to characterize the behaviour of the rock materials and the support used (metal frames and shotcrete). With this information, different hypotheses of failure were simulated to find the one that best fits reality. For this work, the software of finite differences in three dimensions, FLAC 3D, was employed. The results of the study confirmed that the detachment was originated as a consequence of one sliding in the layer wall, due to the large roof span present in the place of the accident, and probably triggered as a consequence of the existence of a protection pillar insufficient. The results allowed to establish some corrective measures avoiding future risks. For example, the dimensions of the protection zones that must be remained unexploited and their interaction with the crossing areas between galleries, or the use of more adequate supports for these conditions, in which the significant deformations may discourage the use of rigid supports such as shotcrete. At last, a grid of seismic control was proposed as a predictive system. Its efficiency was tested along the investigation period employing three control equipment that detected new incidents (although smaller) in other similar areas of the mine. These new incidents show that the use of explosives produces vibrations which are a new risk factor to analyse in a next future.

Keywords: forensic analysis, hypothesis modelling, roof failure, seismic monitoring

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Authors: Ann Devitt, Mairead Brady, Markus Lamest, Stephen Gomez

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This study set out to explore the use of an online collaborative serious game for student learning in a postgraduate introductory marketing module. The simulation game aimed to bridge the theory-practice divide in marketing by allowing students to apply theory in a safe, simulated marketplace. This study addresses the following research questions: Does an online marketing simulation game engage students higher order cognitive skills? Does collaborative activity required develop students’ “soft” skills, such as communication and negotiation? What specific affordances of the online simulation promote learning? This qualitative case study took place in 2014 with 40 postgraduate students on a Business Masters Programme. The two-week intensive module combined lectures with collaborative activity on a marketing simulation game, MMX from Pearsons. The game requires student teams to compete against other teams in a marketplace and design a marketing plan to maximize key performance indicators. The data for this study comprise essays written by students after the module reflecting on their learning on the module. A thematic analysis was conducted of the essays using the following a priori theme sets: 6 levels of the cognitive domain of Blooms taxonomy; 5 principles of Cooperative Learning; affordances of simulation environments including experiential learning; motivation and engagement; goal orientation. Preliminary findings would strongly suggest that the game facilitated students identifying the value of theory in practice, in particular for future employment; enhanced their understanding of group dynamics and their role within that; and impacted very strongly, both positively and negatively on motivation. In particular the game mechanics of MMX, which hinges on the correct identification of a target consumer group, was identified as a key determinant of extrinsic and intrinsic motivation for learners. The findings also suggest that the situation of the simulation game within a broader module which required post-game reflection was valuable in identifying key learning of marketing concepts in both the positive and the negative experiences of the game.

Keywords: simulation, marketing, serious game, cooperative learning, bloom's taxonomy

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Authors: Talal Alsulaiman, Khaldoun Khashanah

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In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent's attributes. Also, the influence of social networks in the developing of agents’ interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: artificial stock markets, market dynamics, bounded rationality, agent based simulation, learning, interaction, social networks

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Authors: Sandra Baez, Mariana Pino, Mildred Berrio, Hernando Santamaria-Garcia, Lucas Sedeno, Adolfo Garcia, Sol Fittipaldi, Agustin Ibanez

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Schadenfreude refers to the perceiver’s experience of pleasure at another’s misfortune. This is a multidetermined emotion which can be evoked by hostile feelings and envy. The experience of Schadenfreude engages mechanisms implicated in diverse social cognitive processes. For instance, Schadenfreude involves heightened reward processing, accompanied by increased striatal engagement and it interacts with mentalizing and perspective-taking abilities. Patients with Huntington's disease (HD) exhibit reductions of Schadenfreude experience, suggesting a role of striatal degeneration in such an impairment. However, no study has directly assessed the relationship between regional brain atrophy in HD and reduced Schadenfreude. This study investigated whether gray matter (GM) atrophy in HD patients correlates with ratings of Schadenfreude. First, we compared the performance of 20 HD patients and 23 controls on an experimental task designed to trigger Schadenfreude and envy (another social emotion acting as a control condition). Second, we compared GM volume between groups. Third, we examined brain regions where atrophy might be associated with specific impairments in the patients. Results showed that while both groups showed similar ratings of envy, HD patients reported lower Schadenfreude. The latter pattern was related to atrophy in regions of the reward system (ventral striatum) and the mentalizing network (precuneus and superior parietal lobule). Our results shed light on the intertwining of reward and socioemotional processes in Schadenfreude, while offering novel evidence about their neural correlates. In addition, our results open the door to future studies investigating social emotion processing in other clinical populations characterized by striatal or mentalizing network impairments (e.g., Parkinson’s disease, schizophrenia, autism spectrum disorders).

Keywords: envy, Gray matter atrophy, Huntigton's disease, Schadenfreude, social emotions

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Authors: C. Busuioc, I. Stancu, A. Nicoara, A. Zamfirescu, A. Evanghelidis

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The field of tissue engineering has expanded its potential due to the use of composite biomaterials belonging to increasingly complex systems, leading to bone substitutes with properties that are continuously improving to meet the patient's specific needs. Furthermore, the development of biomaterials based on ceramic and polymeric phases is an unlimited resource for future scientific research, with the final aim of restoring the original tissue functionality. Thus, in the first stage, composite scaffolds based on polycaprolactone (PCL) or polylactic acid (PLA) and inorganic powders were prepared by employing the electrospinning technique. The targeted powders were: commercial and laboratory synthesized hydroxyapatite (HAp), as well as barium titanate (BT). By controlling the concentration of the powder within the precursor solution, together with the processing parameters, different types of three-dimensional architectures were achieved. In the second stage, both the mineral powders and hybrid composites were investigated in terms of composition, crystalline structure, and microstructure so that to demonstrate their suitability for tissue engineering applications. Regarding the scaffolds, these were proven to be homogeneous on large areas and loaded with mineral particles in different proportions. The biological assays demonstrated that the addition of inorganic powders leads to modified responses in the presence of simulated body fluid (SBF) or cell cultures. Through SBF immersion, the biodegradability coupled with bioactivity were highlighted, with fiber fragmentation and surface degradation, as well as apatite layer formation within the testing period. Moreover, the final composites represent supports accepted by the cells, favoring implant integration. Concluding, the purposed fibrous materials based on bioresorbable polymers and mineral powders, produced by the electrospinning technique, represent candidates with considerable potential in the field of tissue engineering. Future improvements can be attained by optimizing the synthesis process or by simultaneous incorporation of multiple inorganic phases with well-defined biological action in order to fabricate multifunctional composites.

Keywords: barium titanate, electrospinning, fibre networks, hydroxyapatite, smart scaffolds

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Authors: Omar Nasani, Chaza Kouchaji, Muznah Alkhani, Maisaa Abd-alkareem

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Objective: To evaluate the influence of sweetening agents used in pediatric medications on the growth of Streptococcus mutans colonies and its effect on the cariogenic activity in the oral cavity. No previous studies are registered yet in Syrian children. Methods: Specimens were isolated from the oral cavity of pediatric patients, then in-vitro study is applied on locally manufactured liquid pediatric antibiotic drugs, containing natural or synthetic sweeteners. The selected antibiotics are Ampicillin (sucrose), Amoxicillin (sucrose), Amoxicillin + Flucloxacillin (sorbitol), Amoxicillin+Clavulanic acid (Sorbitol or sucrose). These antibiotics have a known inhibitory effect on gram positive aerobic/anaerobic bacteria especially Streptococcus mutans strains in children’s oral biofilm. Five colonies are studied with each antibiotic. Saturated antibiotics were spread on a 6mm diameter filter disc. Incubated culture media were compared with each other and with the control antibiotic discs. Results were evaluated by measuring the diameter of the inhibition zones. The control group of antibiotic discs was resourced from Abtek Biologicals Ltd. Results: The diameter of inhibition zones around discs of antibiotics sweetened with sorbitol was larger than those sweetened with sucrose. The effect was most important when comparing Amoxicillin + Clavulanic Acid (sucrose 25mm; versus sorbitol 27mm). The highest inhibitory effect was observed with the usage of Amoxicillin + Flucloxacillin sweetened with sorbitol (38mm). Whereas the lowest inhibitory effect was observed with Amoxicillin and Ampicillin sweetened with sucrose (22mm and 21mm). Conclusion: The results of this study indicate that although all selected antibiotic produced an inhibitory effect on S. mutans, sucrose weakened the inhibitory action of the antibiotic to varying degrees, meanwhile antibiotic formulations containing sorbitol simulated the effects of the control antibiotic. This study calls attention to effects of sweeteners included in pediatric drugs on the oral hygiene and tooth decay.

Keywords: pediatric, dentistry, antibiotics, streptococcus mutans, biofilm, sucrose, sugar free

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Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

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Authors: Onyekachi Ogbonnaya Iroanya, Taiye Abdullahi Gegele, Frank Tochukwu Egwuatu

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Introduction: Forensic taphonomy is a multifaceted area that incorporates decomposition, chemical and biological cadaver exposure in post-mortem event chronology and reconstruction to predict the Post Mortem Interval (PMI). The aim of this study was to evaluate the integrity of DNA extracted from the remains of embalmed decomposed Sus domesticus tissues buried in different soil types. Method: A total of 12 limbs of Sus domesticus weighing between 0.7-1.4 kg were used. Each of the samples across the groups was treated with 10% formaldehyde, absolute methanol and 50% Pine oil for 24 hours before burial except the control samples, which were buried immediately. All samples were buried in shallow simulated Clay, Sandy and Loamy soil graves for 12 months. The DNA for each sample was extracted and quantified with Nanodrop Spectrophotometer (6305 JENWAY spectrometers). The rate of decomposition was examined through the modified qualitative decomposition analysis. Extracted DNA was amplified through PCR and bands visualized via gel electrophoresis. A biochemical enzyme assay was done for each burial grave soil. Result: The limbs in all burial groups had lost weight over the burial period. There was a significant increase in the soil urease level in the samples preserved in formaldehyde across the 3 soil type groups (p≤0.01). Also, the control grave soils recorded significantly higher alkaline phosphatase, dehydrogenase and calcium carbonate values compared to experimental grave soils (p≤0.01). The experimental samples showed a significant decrease in DNA concentration and purity when compared to the control groups (p≤0.01). Obtained findings of the soil biochemical analysis showed the embalming treatment altered the relationship between organic matter decomposition and soil biochemical properties as observed in the fluctuations that were recorded in the soil biochemical parameters. The PCR amplified DNA showed no bands on the gel electrophoresis plates. Conclusion: In criminal investigations, factors such as burial grave soil, grave soil biochemical properties, antemortem exposure to embalming chemicals should be considered in post-mortem interval (PMI) determination.

Keywords: forensic taphonomy, post-mortem interval (PMI), embalmment, decomposition, grave soil

Procedia PDF Downloads 152