Search results for: link prediction

Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

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Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

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Authors: M. Abou-Amouna, A. Radwan, L. Al-kuwari, A. Hammuda, K. Al-Khalifa

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There is growing concern over increasing incidences of road accidents and consequent loss of human life in Qatar. In light to the future planned event in Qatar, World Cup 2022; Qatar should put into consideration the future deaths caused by road accidents, and past trends should be considered to give a reasonable picture of what may happen in the future. Qatar roads should be arranged and paved in a way that accommodate high capacity of the population in that time, since then there will be a huge number of visitors from the world. Qatar should also consider the risk issues of road accidents raised in that period, and plan to maintain high level to safety strategies. According to the increase in the number of road accidents in Qatar from 1995 until 2012, an analysis of elements affecting and causing road accidents will be effectively studied. This paper aims to identify and criticize the factors that have high effect on causing road accidents in the state of Qatar, and predict the total number of road accidents in Qatar 2022. Alternative methods are discussed and the most applicable ones according to the previous researches are selected for further studies. The methods that satisfy the existing case in Qatar were the multiple linear regression model (MLR) and artificial neutral network (ANN). Those methods are analyzed and their findings are compared. We conclude that by using MLR the number of accidents in 2022 will become 355,226 accidents, and by using ANN 216,264 accidents. We conclude that MLR gave better results than ANN because the artificial neutral network doesn’t fit data with large range varieties.

Keywords: road safety, prediction, accident, model, Qatar

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Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

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Authors: Chia-Jung Li, Kuan-Hao Tsui

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As women age, the quality of their oocytes deteriorates irreversibly, leading to reduced fertility. To better understand the role of Ferroptosis-related genes in ovarian aging, we employed a multi-omics analysis approach, including spatial transcriptomics, single-cell RNA sequencing, human ovarian pathology, and clinical biopsies. Our study identified excess lipid peroxide accumulation in aging germ cells, metal ion accumulation via oxidative reduction, and the interaction between ferroptosis and cellular energy metabolism. We used multi-histological prediction of ferroptosis key genes to evaluate 75 patients with ovarian aging insufficiency and then analyzed changes in hub genes after supplementing with DHEA, Ubiquinol CoQ10, and Cleo-20 T3 for two months. Our results demonstrated a significant increase in TFRC, GPX4, NCOA4, and SLC3A2, which were consistent with our multi-component prediction. We theorized that these supplements increase the mitochondrial tricarboxylic acid cycle (TCA) or electron transport chain (ETC), thereby increasing antioxidant enzyme GPX4 levels and reducing lipid peroxide accumulation and ferroptosis. Overall, our findings suggest that supplementation intervention significantly improves IVF outcomes in senescent cells by enhancing metal ion and energy metabolism and enhancing oocyte quality in aging women.

Keywords: multi-omics, nutrients, ferroptosis, ovarian aging

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Authors: Bi-Huei Tsai

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Previous studies on financial distress prediction choose the conventional failing and non-failing dichotomy; however, the distressed extent differs substantially among different financial distress events. To solve the problem, “non-distressed”, “slightly-distressed” and “reorganization and bankruptcy” are used in our article to approximate the continuum of corporate financial health. This paper explains different financial distress events using the two-stage method. First, this investigation adopts firm-specific financial ratios, corporate governance and market factors to measure the probability of various financial distress events based on multinomial logit models. Specifically, the bootstrapping simulation is performed to examine the difference of estimated misclassifying cost (EMC). Second, this work further applies macroeconomic factors to establish the credit cycle index and determines the distressed cut-off indicator of the two-stage models using such index. Two different models, one-stage and two-stage prediction models, are developed to forecast financial distress, and the results acquired from different models are compared with each other, and with the collected data. The findings show that the two-stage model incorporating financial ratios, corporate governance and market factors has the lowest misclassification error rate. The two-stage model is more accurate than the one-stage model as its distressed cut-off indicators are adjusted according to the macroeconomic-based credit cycle index.

Keywords: Multinomial logit model, corporate governance, company failure, reorganization, bankruptcy

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Authors: Jongsung Kim, Daegun Han, Myungjin Lee, Soojun Kim, Hung Soo Kim

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Recently, the frequency and magnitude of natural disasters are gradually increasing due to climate change. Especially in Korea, large-scale damage caused by heavy rainfall frequently occurs due to rapid urbanization. Therefore, this study proposed a Heavy rain Damage Risk Index (HDRI) using PSR (Pressure – State - Response) structure for heavy rain risk assessment. We constructed pressure index, state index, and response index for the risk assessment of each local government in Gyeonggi-do province, and the evaluation indices were determined by principal component analysis. The indices were standardized using the Z-score method then HDRIs were obtained for 31 local governments in the province. The HDRI is categorized into three classes, say, the safest class is 1st class. As the results, the local governments of the 1st class were 15, 2nd class 7, and 3rd class 9. From the study, we were able to identify the risk class due to the heavy rainfall for each local government. It will be useful to develop the heavy rainfall prediction function by risk class, and this was performed in this issue. Also, this risk class could be used for the decision making for efficient disaster management. Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B3005695).

Keywords: natural disaster, heavy rain risk assessment, HDRI, PSR

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Authors: C. E. Sutton, A. Varvani-Farahani

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Fatigue damage and life prediction of particle metal matrix composites (PMMCs) under uniaxial and multiaxial loading conditions were investigated. Three PMM composite materials of Al6061/Al2O3/20p-T6, Al6061/Al2O3/22p-T6 and Al6061/SiC/17w-T6 tested under tensile, torsion, and combined tension-torsion fatigue cycling were evaluated with various fatigue damage models. The fatigue damage models of Smith-Watson-Topper (S. W. T.), Ellyin, Brown-Miller, Fatemi-Socie, and Varvani were compared for their capability to assess the fatigue damage of materials undergoing various loading conditions. Fatigue life predication results were then evaluated by implementing material-dependent coefficients that factored in the effects of the particle reinforcement in the earlier developed Varvani model. The critical plane-energy approach incorporated the critical plane as the plane of crack initiation and early stage of crack growth. The strain energy density was calculated on the critical plane incorporating stress and strain components acting on the plane. This approach successfully evaluated fatigue damage values versus fatigue lives within a narrower band for both uniaxial and multiaxial loading conditions as compared with other damage approaches studied in this paper.

Keywords: fatigue damage, life prediction, critical plane approach, energy approach, PMM composites

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Authors: Gloria Patricia Manurung

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Jakarta Bus Rapid Transit (BRT) system which was established in 2009 to reduce private vehicle usage and ease the rush hour gridlock throughout the Jakarta Greater area, has failed to achieve its purpose. With gradually increasing the number of private vehicles ownership and reduced road space by the BRT lane construction, private vehicle users intuitively invade the exclusive lane of BRT, creating local traffic along the BRT network. Invaded BRT lanes costs become the same with the road network, making BRT which is supposed to be the main public transportation in the city becoming unreliable. Efforts to guard critical lanes with preventing the invasion by allocating traffic agents at several intersections have been expended, lead to the improving congestion level along the lane. Given a set of number of traffic agents, this study uses an analytical approach to finding the best deployment strategy of traffic agent on a simplified Jakarta road network in minimizing the BRT link cost which is expected to lead to the improvement of BRT system time reliability. User-equilibrium model of traffic assignment is used to reproduce the origin-destination demand flow on the network and the optimum solution conventionally can be obtained with brute force algorithm. This method’s main constraint is that traffic assignment simulation time escalates exponentially with the increase of set of agent’s number and network size. Our proposed metaheuristic and heuristic algorithms perform linear simulation time increase and result in minimized BRT cost approaching to brute force algorithm optimization. Further analysis of the overall network link cost should be performed to see the impact of traffic agent deployment to the network system.

Keywords: traffic assignment, user equilibrium, greedy algorithm, optimization

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Ahmad Shahin, Walid Moudani, Ali Bekraki

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This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources.

Keywords: healthcare management, acute appendicitis, data mining, classification, decision tree

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Manop Aorpimai, Ponthep Navakitkanok

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In this paper, the development of the ephemeris generation module used for the Thaichote satellite operations is presented. It is a vital part of the flight dynamics system, which comprises, the orbit determination, orbit propagation, event prediction and station-keeping maneuver modules. In the generation of the spacecraft ephemeris data, the estimated orbital state vector from the orbit determination module is used as an initial condition. The equations of motion are then integrated forward in time to predict the satellite states. The higher geopotential harmonics, as well as other disturbing forces, are taken into account to resemble the environment in low-earth orbit. Using a highly accurate numerical integrator based on the Burlish-Stoer algorithm the ephemeris data can be generated for long-term predictions, by using a relatively small computation burden and short calculation time. Some events occurring during the prediction course that are related to the mission operations, such as the satellite’s rise/set viewed from the ground station, Earth and Moon eclipses, the drift in ground track as well as the drift in the local solar time of the orbital plane are all detected and reported. When combined with other modules to form a flight dynamics system, this application is aimed to be applied for the Thaichote satellite and successive Thailand’s Earth-observation missions.

Keywords: flight dynamics system, orbit propagation, satellite ephemeris, Thailand’s Earth Observation Satellite

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Authors: Hamidreza Hoshyarmanesh, Sanju Lama, Garnette R. Sutherland

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In tele-operative robotic surgery, an ideal haptic device should be equipped with an intuitive and smooth end-effector to cover the surgeon’s hand/wrist degrees of freedom (DOF) and translate the hand joint motions to the end-effector of the remote manipulator with low effort and high level of comfort. This research introduces the design and development of a microsurgery-specific end-effector, a gimbal mechanism possessing 4 passive and 1 active DOFs, equipped with a bipolar forceps and haptic feedback. The robust gimbal structure is comprised of three light-weight links/joint, pitch, yaw, and roll, each consisting of low-friction support and a 2-channel accurate optical position sensor. The third link, which provides the tool roll, was specifically designed to grip the tool prongs and accommodate a low mass geared actuator together with a miniaturized capstan-rope mechanism. The actuator is able to generate delicate torques, using a threaded cylindrical capstan, to emulate the sense of pinch/coagulation during conventional microsurgery. While the tool left prong is fixed to the rolling link, the right prong bears a miniaturized drum sector with a large diameter to expand the force scale and resolution. The drum transmits the actuator output torque to the right prong and generates haptic force feedback at the tool level. The tool is also equipped with a hall-effect sensor and magnet bar installed vis-à-vis on the inner side of the two prongs to measure the tooltip distance and provide an analogue signal to the control system. We believe that such a haptic end-effector could significantly increase the accuracy of telerobotic surgery and help avoid high forces that are known to cause bleeding/injury.

Keywords: end-effector, force generation, haptic interface, robotic surgery, surgical tool, tele-operation

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Authors: Emmanuel Okechukwu Nwafor, Folake Olubunmi Akintayo, Denis Alcides Rezende

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Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria.

Keywords: decision tree algorithm, trip purpose, intelligent transport, strategic digital city, travel pattern, sustainable transport

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Authors: Anurag Tripathi, Sanad Khandelwal

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Age and gender determination are required in forensic for victim identification. There is secondary dentine deposition throughout life, resulting in decreased pulp volume and size. Evaluation of pulp volume using Cone Beam Computed Tomography (CBCT)is a noninvasive method to evaluate the age and gender of an individual. The study was done to evaluate the efficacy of pulp volume method in the determination of age and gender.Aims/Objectives: The study was conducted to estimate age and determine sex by measuring tooth pulp volume with the help of CBCT. An observational study of one year duration on CBCT data of individuals was conducted in Lucknow. Maxillary central incisors (CI) and maxillary canine (C) of the randomly selected samples were assessed for measurement of pulp volume using a software. Statistical analysis: Chi Square Test, Arithmetic Mean, Standard deviation, Pearson’s Correlation, Linear & Logistic regression analysis. Results: The CBCT data of Ninety individuals with age range between 18-70 years was evaluated for pulp volume of central incisor and canine (CI & C). The Pearson correlation coefficient between the tooth pulp volume (CI & C) and chronological age suggested that pulp volume decreased with age. The validation of the equations for sex determination showed higher prediction accuracy for CI (56.70%) and lower for C (53.30%).Conclusion: Pulp volume obtained from CBCT is a reliable indicator for age estimation and gender prediction.

Keywords: forensic, dental age, pulp volume, cone beam computed tomography

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Authors: Semere Araya Asefa

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I described twisted bilayer crescent metasurfaces that link optical properties between two layers and enhance circular dichroism. The interactions between the metasurface layers cause circular dichroism. And we evaluated the parameters that affect the chiroptical response of the crescent

Keywords: chiroptical response, chiral metasurface, circular dichroism, chiral sensing

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Authors: Nitin Dewangan, Nitin Kattula, Megha Anawat

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Hydraulic reservoir is the key component in the mobile construction vehicles; most of the off-road earth moving construction machinery requires bigger side hydraulic reservoirs. Their reservoir construction is very much non-uniform and designers used such design to utilize the space available under the vehicle. There is no way to find out the space utilization of the reservoir by oil and validity of design except virtual simulation. Computational fluid dynamics (CFD) helps to predict the reservoir space utilization by vortex mapping, path line plots and dwell time prediction to make sure the design is valid and efficient for the vehicle. The dwell time acceptance criteria for effective reservoir design is 15 seconds. The paper will describe the hydraulic reservoir simulation which is carried out using CFD tool acuSolve using automated mesh strategy. The free surface flow and moving reference mesh is used to define the oil flow level inside the reservoir. The first baseline design is not able to meet the acceptance criteria, i.e., dwell time below 15 seconds because the oil entry and exit ports were very close. CFD is used to redefine the port locations for the reservoir so that oil dwell time increases in the reservoir. CFD also proposed baffle design the effective space utilization. The final design proposed through CFD analysis is used for physical validation on the machine.

Keywords: reservoir, turbulence model, transient model, level set, free-surface flow, moving frame of reference

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Authors: Georges Ghazi, Magali Gelhaye, Ruxandra Botez

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Over the years, the Flight Management System (FMS) has experienced a continuous improvement of its many features, to the point of becoming the pilot’s primary interface for flight planning operation on the airplane. With the assistance of the FMS, the concept of distance and time has been completely revolutionized, providing the crew members with the determination of the optimized route (or flight plan) from the departure airport to the arrival airport. To accomplish this function, the FMS needs an accurate Aircraft Performance Model (APM) of the aircraft. In general, APMs that equipped most modern FMSs are established before the entry into service of an individual aircraft, and results from the combination of a set of ordinary differential equations and a set of performance databases. Unfortunately, an aircraft in service is constantly exposed to dynamic loads that degrade its flight characteristics. These degradations endow two main origins: airframe deterioration (control surfaces rigging, seals missing or damaged, etc.) and engine performance degradation (fuel consumption increase for a given thrust). Thus, after several years of service, the performance databases and the APM associated to a specific aircraft are no longer representative enough of the actual aircraft performance. It is important to monitor the trend of the performance deterioration and correct the uncertainties of the aircraft model in order to improve the accuracy the flight management system predictions. The basis of this research lies in the new ability to continuously update an Aircraft Performance Model (APM) during flight using an adaptive lookup table technique. This methodology was developed and applied to the well-known Cessna Citation X business aircraft. For the purpose of this study, a level D Research Aircraft Flight Simulator (RAFS) was used as a test aircraft. According to Federal Aviation Administration the level D is the highest certification level for the flight dynamics modeling. Basically, using data available in the Flight Crew Operating Manual (FCOM), a first APM describing the variation of the engine fan speed and aircraft fuel flow w.r.t flight conditions was derived. This model was next improved using the proposed methodology. To do that, several cruise flights were performed using the RAFS. An algorithm was developed to frequently sample the aircraft sensors measurements during the flight and compare the model prediction with the actual measurements. Based on these comparisons, a correction was performed on the actual APM in order to minimize the error between the predicted data and the measured data. In this way, as the aircraft flies, the APM will be continuously enhanced, making the FMS more and more precise and the prediction of trajectories more realistic and more reliable. The results obtained are very encouraging. Indeed, using the tables initialized with the FCOM data, only a few iterations were needed to reduce the fuel flow prediction error from an average relative error of 12% to 0.3%. Similarly, the FCOM prediction regarding the engine fan speed was reduced from a maximum error deviation of 5.0% to 0.2% after only ten flights.

Keywords: aircraft performance, cruise, trajectory optimization, adaptive lookup tables, Cessna Citation X

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Authors: Mohamed Boly

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This paper investigates the link between foreign aid and environmental protection, specifically CO2 emissions, in aid recipient countries. Conflicting results exist in the literature regarding the environmental impact of foreign aid. We come to reconcile them, using Project-Level Aid Data with environment codes, over the 1980- 2010 period. The disaggregation of aid according to the environmental codes, show why the results of previous literature remain very mixed. Moreover, we find that the effect of environmental aid is conditioned by some specific characteristics of the recipient country, independently of the donor.

Keywords: foreign aid, green aid, interactive effects, pollution

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Nebila Lichiheb, LaToya Myles, William Pendergrass, Bruce Hicks, Dawson Cagle

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Atmospheric transport of hazardous materials in urban areas is increasingly under investigation due to the potential impact on human health and the environment. In response to health and safety concerns, several dispersion models have been developed to analyze and predict the dispersion of hazardous contaminants. The models of interest usually rely on meteorological information obtained from the meteorological models of NOAA’s National Weather Service (NWS). However, due to the complexity of the urban environment, NWS forecasts provide an inadequate basis for dispersion computation in urban areas. A dense meteorological network in Washington, DC, called DCNet, has been operated by NOAA since 2003 to support the development of urban monitoring methodologies and provide the driving meteorological observations for atmospheric transport and dispersion models. This study focuses on the comparison of wind observations from the DCNet station on the U.S. Department of Commerce Herbert C. Hoover Building against the North American Mesoscale (NAM) model outputs for the period 2017-2019. The goal is to develop a simple methodology for modifying NAM outputs so that the dispersion requirements of the city and its urban area can be satisfied. This methodology will allow us to quantify the prediction errors of the NAM model and propose adjustments of key variables controlling dispersion model calculation.

Keywords: meteorological data, Washington D.C., DCNet data, NAM model

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Authors: K. A. Bindu, T. V. Raja, P. M. Rojan, A. Siby

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The multivariate path coefficient approach was employed to study the effects of various production and reproduction traits on the slaughter body weight of rabbits. Information on 562 rabbits maintained at the university rabbit farm attached to the Centre for Advanced Studies in Animal Genetics, and Breeding, Kerala Veterinary and Animal Sciences University, Kerala State, India was utilized. The manifest variables used in the study were age and weight of dam, birth weight, litter size at birth and weaning, weight at first, second and third months. The linear multiple regression analysis was performed by keeping the slaughter weight as the dependent variable and the remaining as independent variables. The model explained 48.60 percentage of the total variation present in the market weight of the rabbits. Even though the model used was significant, the standardized beta coefficients for the independent variables viz., age and weight of the dam, birth weight and litter sizes at birth and weaning were less than one indicating their negligible influence on the slaughter weight. However, the standardized beta coefficient of the second-month body weight was maximum followed by the first-month weight indicating their major role on the market weight. All the other factors influence indirectly only through these two variables. Hence it was concluded that the slaughter body weight can be predicted using the first and second-month body weights. The principal components were also developed so as to achieve more accuracy in the prediction of market weight of rabbits.

Keywords: component analysis, multivariate, slaughter, regression

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Authors: Welawat Tienpratarn, Chaiyaporn Yuksen, Rungrawin Promkul, Chetsadakon Jenpanitpong, Pajit Bunta, Suthap Jaiboon

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Supraventricular tachycardia (SVT) is an abnormally fast atrial tachycardia characterized by narrow (≤ 120 ms) and constant QRS. Adenosine was the drug of choice; the first dose was 6 mg. It can be repeated with the second and third doses of 12 mg, with greater than 90% success. The study found that patients observed at 4 hours after normal sinus rhythm was no recurrence within 24 hours. The objective of this study was to investigate the factors that influence the recurrence of SVT after adenosine in the emergency department (ED). The study was conducted retrospectively exploratory model, prognostic study at the Emergency Department (ED) in Faculty of Medicine, Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand. The study was conducted for ten years period between 2010 and 2020. The inclusion criteria were age > 15 years, visiting the ED with SVT, and treating with adenosine. Those patients were recorded with the recurrence SVT in ED. The multivariable logistic regression model developed the predictive model and prediction score for recurrence PSVT. 264 patients met the study criteria. Of those, 24 patients (10%) had recurrence PSVT. Five independent factors were predictive of recurrence PSVT. There was age>65 years, heart rate (after adenosine) > 100 per min, structural heart disease, and dose of adenosine. The clinical risk score to predict recurrence PSVT is developed accuracy 74.41%. The score of >6 had the likelihood ratio of recurrence PSVT by 5.71 times. The clinical predictive score of > 6 was associated with recurrence PSVT in ED.

Keywords: supraventricular tachycardia, recurrance, emergency department, adenosine

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Authors: Ugur Choban, Aruzhan Zhaksylyk, Assylbek Nurgabdeshov

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In recent years, there has been an increasing understanding of the value of encouraging entrepreneurial attitudes in university students. This is motivated by the belief that stimulating entrepreneurship not only promotes economic growth but also fosters innovation. This study looks at the complex link and addresses critical gaps between psychological capital and entrepreneurial intention among university students, with a specific emphasis on how contextual factors like academic support and past business experience impact this dynamic. Using a quantitative research method, data were gathered from a broad sample of 300 university students drawn from several faculties. The study used a questionnaire that included the Psychological Capital Questionnaire (PCQ) to assess psychological capital and a validated scale for entrepreneurial intention, as well as binary measures of academic support and prior entrepreneurial experience. Statistical investigations, including multigroup analyses performed with SmartPLS software, provided interesting insights into the effect of contextual factors on the relationship between psychological capital and entrepreneurial intention. The findings highlight that psychological capital had a strong favorable influence on university students' entrepreneurial inclinations. Furthermore, the study found that academic support enhances the influence of psychological capital on entrepreneurial intentions, emphasizing the significance of institutional backing in fostering entrepreneurial mindsets. Furthermore, students with prior entrepreneurial experience had a stronger propensity for entrepreneurship, showing a synergistic link between psychological capital and entrepreneurial background. These findings have both theoretical and practical implications. By explaining the mechanisms by which psychological capital promotes entrepreneurial intentions, the study contributes to the establishment of focused entrepreneurship education programs and support activities that are suited to student requirements. Policymakers may use these findings to create policies that encourage student entrepreneurship, ultimately encouraging economic development and innovation.

Keywords: academic support, entrepreneurial intentions, higher education institutions, psychological capital, prior entrepreneurial experience

Procedia PDF Downloads 52

Authors: Vasujeet Singh, Pruthiviraj Nemalipuri, Vivek Vitankar, Harish Chandra Das

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For the correct prediction of thermal and hydraulic performance (bed voidage, suspension density, pressure drop, heat transfer, and combustion kinetics), one should incorporate the correct parameters in the computational fluid dynamics simulation of a fluidized bed gasifier. Scarcity of fossil fuels, and to fulfill the energy demand of the increasing population, researchers need to shift their attention to the alternative to fossil fuels. The current research work focuses on hydrodynamics behavior and gasification of sawdust inside a 2D industrial scale FBG using the Eulerian-Eulerian multifluid model. The present numerical model is validated with experimental data. Further, this model extended for the prediction of gasification characteristics of sawdust by incorporating eight heterogeneous moisture release, volatile cracking, tar cracking, tar oxidation, char combustion, CO₂ gasification, steam gasification, methanation reaction, and five homogeneous oxidation of CO, CH₄, H₂, forward and backward water gas shift (WGS) reactions. In the result section, composition of gasification products is analyzed, along with the hydrodynamics of sawdust and sand phase, heat transfer between the gas, sand and sawdust, reaction rates of different homogeneous and heterogeneous reactions is being analyzed along the height of the domain.

Keywords: devolatilization, Eulerian-Eulerian, fluidized bed gasifier, mathematical modelling, sawdust gasification

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Authors: Sang-Woo Kim, Young-Seon Lee

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An important concern in metal forming, even at elevated temperatures, is whether a desired deformation can be accomplished without any failure of the material. A detailed understanding of the critical condition for crack initiation provides not only the workability limit of a material but also a guide-line for process design. This paper describes the utilization of ductile fracture criteria in conjunction with the finite element method (FEM) for predicting the onset of fracture in warm metal working processes of magnesium alloy sheets. Critical damage values for various ductile fracture criteria were determined from uniaxial tensile tests and were expressed as the function of strain rate and temperature. In order to find the best criterion for failure prediction, Erichsen cupping tests under isothermal conditions and FE simulations combined with ductile fracture criteria were carried out. Based on the plastic deformation histories obtained from the FE analyses of the Erichsen cupping tests and the critical damage value curves, the initiation time and location of fracture were predicted under a bi-axial tensile condition. The results were compared with experimental results and the best criterion was recommended. In addition, the proposed methodology was used to predict the onset of fracture in non-isothermal deep drawing processes using an irregular shaped blank, and the results were verified experimentally.

Keywords: magnesium, AZ31 alloy, ductile fracture, FEM, sheet forming, Erichsen cupping test

Procedia PDF Downloads 371