Search results for: injury prediction

Authors: Etienne Alain Feukeu, L. W. Snyman

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Water scarcity is becoming a real issue nowadays. Most countries in the world are facing it in their own way based on their own geographical coordinate and condition. Many countries are facing a challenge of a growing water demand as a result of not only an increased population, economic growth, but also as a pressure of the population dynamic and urbanization. In view to mitigate some of this related problem, an accurate method of water estimation and future prediction, forecast is essential to guarantee not only the sufficient quantity, but also a good water distribution and management system. Beside the fact that several works have been undertaken to address this concern, there is still a considerable disparity between different methods and standard used for water prediction and estimation. Hence this work contrast and compare two well-defined and established methods from two countries (USA and South Africa) to demonstrate the inconsistency when different method and standards are used interchangeably.

Keywords: water scarcity, water estimation, water prediction, water forecast.

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Authors: Nisha Nandlal Sharma, Julaluk Carmai, Saiprasit Koetniyom, Bernd Markert

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Whiplash Injuries are mostly experienced in car accidents. Symptoms of whiplash are commonly reported in studies, neck pain and headaches are two most common symptoms observed. The whiplash Injury mechanism is poorly understood. In present study, hybrid neck muscle model were developed with a combination of solid tetrahedral elements and 1D beam elements. Solid tetrahedral elements represents passive part of the muscle whereas, 1D beam elements represents active part. To simulate the active behavior of the muscle, Hill-type muscle model was applied to beam elements. To simulate non-linear passive properties of muscle, solid elements were modeled with rubber/foam material model. Some important muscles were then inserted into THUMS (Total Human Model for Safety) THUMS was given a boundary conditions similar to experimental tests. The model was exposed to 4g and 7g rear impacts as these load impacts are close to low speed impacts causing whiplash. The effect of muscle activation level on occupant kinematics during whiplash was analyzed.

Keywords: finite element model, muscle activation, THUMS, whiplash injury mechanism

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Authors: Francis Mark A. Fernandez, Chelca Ubay, Armithan Suguitan

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Regions or provinces in a definite state certainly contribute to the economy of their mainland. These regions or provinces are the ones supplying the mainland with different resources and assets. Thus, with a certain region separating from the mainland would indeed impinge the heart of an entire state to develop and expand. With these, the researchers decided to study on the effects of the separation of one’s region to its mainland and the consequences that will take place if the mainland would rule out the region to separate from them. The researchers wrote this paper to present the causes of the separation of Catalonia from Spain and the prediction regarding the pursuance of this region to revolt from its mainland, Spain. In conducting this research, the researchers utilized two analyses, namely: qualitative and quantitative. In qualitative, numerous of information regarding the existing experiences of the citizens of Catalonia were gathered by the authors to give certainty to the prediction of the researchers. Besides this undertaking, the researchers will also gather needed information and figures through books, journals and the published news and reports. In addition, to further support this prediction under qualitative analysis, the researchers intended to operate the Phenomenological research in which the examiners will exemplify the lived experiences of each citizen in Catalonia. Moreover, the researchers will utilize one of the types of Phenomenological research which is hermeneutical phenomenology by Van Manen. In quantitative analysis, the researchers utilized the regression analysis in which it will ascertain the causality in an underlying theory in understanding the relationship of the variables. The researchers assigned and identified different variables, wherein the dependent variable or the y which represents the prediction of the researchers, the independent variable however or the x represents the arising problems that grounds the partition of the region, the summation of the independent variable or the ∑x represents the sum of the problem and finally the summation of the dependent variable or the ∑y is the result of the prediction. With these variables, using the regression analysis, the researchers will be able to show the connections and how a single variable could affect the other variables. From these approaches, the prediction of the researchers will be specified. This research could help different states dealing with this kind of problem. It will further help certain states undergoing this problem by analyzing the causes of these insurgencies and the effects on it if it will obstruct its region to consign their full-pledge autonomy.

Keywords: autonomy, liberty, prediction, separation

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Authors: Halima Sadia Qureshi, Urooj Sadiq, Noshi Eram Zaman

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The goal of this study was to see how cognitive behavior hypnotherapy affected nonsuicidal self-injury. DSM 5 invites the researchers to explore the newly added condition under the chapter of conditions under further study named Nonsuicidal self-injury disorder. To date, no empirical sound intervention has been proven effective for NSSI as given in DSM 5. Nonsuicidal self-injury is defined by DSM 5 as harming one's self physically, without suicidal intention. Around 7.6% of teenagers are expected to fulfill the NSSI disorder criteria. 3 Adolescents, particularly university students, account for around 87 percent of self-harm studies. Furthermore, one of the risks associated with NSSI is an increased chance of suicide attempts, and in most cases, the cycle repeats again. 6 The emotional and psychological components of the illness might lead to suicide, either intentionally or unintentionally. 7 According to a research done at a Pakistani military hospital, over 80% of participants had no intention of committing suicide. Furthermore, it has been determined that improvements in NSSI prevention and intervention are necessary as a stand-alone strategy. The quasi-experimental study took place in Islamabad and Rawalpindi, Pakistan, from May 2019 to April 2020 and included students aged 18 to 25 years old from several institutions and colleges in the twin cities. According to the Diagnostic and Statistical Manual of Mental Disorders 5th edition, the individuals were assessed for >2 episodes without suicidal intent using the intentional self-harm questionnaire. The Clinician Administered Nonsuicidal Self-Injury Disorder Index (CANDI) was used to assess the individual for NSSI condition. Symptom checklist-90 (SCL-90) was used to screen the participants for differential diagnosis. Mclean Screening Instrument for Borderline Personality Disorder (MSI-BPD) was used to rule out the BPD cases. The selected participants, n=106 from the screening sample of 600, were selected. They were further screened to meet the inclusion and exclusion criteria, and the total of n=71 were split into two groups: intervention and control. The intervention group received cognitive behavior hypnotherapy for the next three months, whereas the control group received no treatment. After the period of three months, both the groups went through the post assessment, and after the three months’ period, follow-up assessment was conducted. The groups were evaluated, and SPSS 25 was used to analyse the data. The results showed that each of the two groups had 30 (50 percent) of the 60 participants. There were 41 males (68 percent) and 19 girls (32 percent) in all. The bulk of the participants were between the ages of 21 and 23. (48 percent). Self-harm events were reported by 48 (80 percent) of the pupils, and suicide ideation was found in 6 (ten percent). In terms of pre- and post-intervention values (d=4.90), post-intervention and follow-up assessment values (d=0.32), and pre-intervention and follow-up values (d=5.42), the study's effect size was good. The comparison of treatment and no-treatment groups revealed that treatment was more successful than no-treatment, F (1, 58) = 53.16, p.001. The results reveal that the treatment manual of CBH is effective for Nonsuicidal self-injury disorder.

Keywords: NSSI, nonsuicidal self injury disorder, self-harm, self-injury, Cognitive behaviour hypnotherapy, CBH

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Authors: D. Mohammadzadeh S., J. Bolouri Bazaz

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This paper presents a new prediction model for compression index of fine-grained soils using multi-gene genetic programming (MGGP) technique. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. Several laboratory test results for fine-grained were used to develop the models. Various criteria were considered to check the validity of the model. The parametric and sensitivity analyses were performed and discussed. The MGGP method was found to be very effective for predicting the soil compression index. A comparative study was further performed to prove the superiority of the MGGP model to the existing soft computing and traditional empirical equations.

Keywords: new prediction model, compression index soil, multi-gene genetic programming, MGGP

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Authors: Nilubon Kurubanjerdjit, Nattakarn Iam-On, Ka-Lok Ng

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MicroRNAs are small non-coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA-target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA-target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA-target interaction. Then, machine-learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation.

Keywords: microRNA, miRNAs, lung cancer, machine learning, Naïve Bayes, SVM

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Authors: Dong Wu, Michael Grenn

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Managing software projects effectively is crucial for meeting deadlines, ensuring quality, and managing resources well. Traditional methods often struggle with predicting project timelines accurately due to uncertain schedules and complex data. This study addresses these challenges by combining time prediction algorithms with Large Language Models (LLMs). It makes use of real-world software project data to construct and validate a model. The model takes detailed project progress data such as task completion dynamic, team Interaction and development metrics as its input and outputs predictions of project timelines. To evaluate the effectiveness of this model, a comprehensive methodology is employed, involving simulations and practical applications in a variety of real-world software project scenarios. This multifaceted evaluation strategy is designed to validate the model's significant role in enhancing forecast accuracy and elevating overall management efficiency, particularly in complex software project environments. The results indicate that the integration of time prediction algorithms with LLMs has the potential to optimize software project progress management. These quantitative results suggest the effectiveness of the method in practical applications. In conclusion, this study demonstrates that integrating time prediction algorithms with LLMs can significantly improve the predictive accuracy and efficiency of software project management. This offers an advanced project management tool for the industry, with the potential to improve operational efficiency, optimize resource allocation, and ensure timely project completion.

Keywords: software project management, time prediction algorithms, large language models (LLMS), forecast accuracy, project progress prediction

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Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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Authors: P. W. G. D. P. Samarasekara, S. M. K. S. Seneviratne, D. Munidasa, S. S. Williams

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Sustaining a spinal cord injury (SCI) causes severe disruption of all aspects of a person’s life, resulting in the difficult process of coping with the distressing effects of paralysis affecting their ability to lead a meaningful life. These persons are hospitalized in the acute stage of injury and subsequently for rehabilitation and the treatment of complications. The purpose of this study was to explore coping strategies used by persons with SCI during their rehabilitation period. A qualitative study was conducted among persons with SCI, undergoing rehabilitation at the Rheumatology and Rehabilitation Hospitals, Ragama and Digana Sri Lanka. Twelve participants were selected purposively to represent both males and females, with cervical, thoracic or lumbar levels of injuries due to traumatic and non-traumatic causes as well as from different socioeconomic backgrounds. Informed consent was taken from the participants. In-depth interviews were conducted using an interview guide to collect data. Probes were used to get more information and to encourage participants. Interviews were audio taped and transcribed verbatim. Qualitative content analysis was conducted. Ethical approval for this study was obtained from the Ethics Review Committee, Faculty of Medicine, University of Kelaniya. Five themes were identified in the content analysis: social support, religious beliefs, determination, acceptance and making comparisons. Participants indicated that the support from their family members had been an essential factor in coping, after sustaining an SCI and they expressed the importance of emotional support from family members during their rehabilitation. Many participants had a strong belief towards the God, who had a personal interest in their lives, played an important role in their ability to cope with the injury. They believed that what happens to them in this life results from their actions in previous lives. They expressed that determination was essential as a factor that helps them cope with their injury. They indicated their focus on the positive aspects of the life and accepted the disability. They made comparisons to other persons who were worse off than them to help lift them out of unpleasant experience. Even some of the most severely injured and disabled participants presented evidence of using this coping strategy. Identification of coping strategies used by persons with SCI will help nurses and other health-care professionals in reinforcing the most effective coping strategies among persons with SCI. The findings recommend that engagement coping positively influences psychosocial adaptation.

Keywords: content analysis, coping strategies, rehabilitation, spinal cord injury

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Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Yenni Kurniawati

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Students Prediction Skill in chemistry experiments is an important skill for pre-service chemistry students to stimulate students reflective thinking at each stage of many chemistry experiments, qualitatively and quantitatively. A Virtual Chemistry Laboratory was designed to give students opportunities and times to practicing many kinds of chemistry experiments repeatedly, everywhere and anytime, before they do a real experiment. The Virtual Chemistry Laboratory content was constructed using the Model of Educational Reconstruction and developed to enhance students ability to predicted the experiment results and analyzed the cause of error, calculating the accuracy and precision with carefully in using chemicals. This research showed students changing in making a decision and extremely beware with accuracy, but still had a low concern in precision. It enhancing students level of reflective thinking skill related to their prediction skill 1 until 2 stage in average. Most of them could predict the characteristics of the product in experiment, and even the result will going to be an error. In addition, they take experiments more seriously and curiously about the experiment results. This study recommends for a different subject matter to provide more opportunities for students to learn about other kinds of chemistry experiments design.

Keywords: virtual chemistry laboratory, chemistry experiments, prediction skill, pre-lab experiences

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Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

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Authors: Xiping Wang，Shuran Yao, Liqin Dai

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Many studies report the winter is getting warmer and the minimum air temperature is obviously rising as the important climate warming evidences. The exacerbated air temperature fluctuation tending to bring more severe weather variation is another important consequence of recent climate change which induced more disasters to crop growth in quite a certain regions. Hebei Province is an important winter wheat growing province in North of China that recently endures more winter freezing injury influencing the local winter wheat crop management. A winter wheat freezing injury assessment Bayesian Network framework was established for the objectives of estimating, assessing and predicting winter wheat freezing disasters in Hebei Province. In this framework, the freezing disasters was classified as three severity degrees (SI) among all the three types of freezing, i.e., freezing caused by severe cold in anytime in the winter, long extremely cold duration in the winter and freeze-after-thaw in early season after winter. The factors influencing winter wheat freezing SI include time of freezing occurrence, growth status of seedlings, soil moisture, winter wheat variety, the longitude of target region and, the most variable climate factors. The climate factors included in this framework are daily mean and range of air temperature, extreme minimum temperature and number of days during a severe cold weather process, the number of days with the temperature lower than the critical temperature values, accumulated negative temperature in a potential freezing event. The Bayesian Network model was evaluated using actual weather data and crop records at selected sites in Hebei Province using real data. With the multi-stage influences from the various factors, the forecast and assessment of the event-based target variables, freezing injury occurrence and its damage to winter wheat production, were shown better scoped by Bayesian Network model.

Keywords: bayesian networks, climatic change, freezing Injury, winter wheat

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Authors: P. Donnelly, G. Mitchell

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Traumatic Brain Injury (TBI) is a major contributor to the global burden of disease; despite its ubiquity, there is significant variation in diagnosis, prognosis, and treatment between clinicians. This study aims to examine the spectrum of approaches that currently exist at a Level 1 Trauma Centre in Australasia by surveying Emergency Physicians and Neurosurgeons on those aspects of mTBI. A pilot survey of 17 clinicians (Neurosurgeons, Emergency Physicians, and others who manage patients with mTBI) at a Level 1 Trauma Centre in Brisbane, Australia, was conducted. The objective of this study was to examine the importance these clinicians place on various elements in their approach to the diagnosis, prognostication, and treatment of mTBI. The data were summarised, and the descriptive statistics reported. Loss of consciousness and post-traumatic amnesia were rated as the most important signs or symptoms in diagnosing mTBI (median importance of 8). MRI was the most important imaging modality in diagnosing mTBI (median importance of 7). ‘Number of the Previous TBIs’ and Intracranial Injury on Imaging’ were rated as the most important elements for prognostication (median importance of 9). Education and reassurance were rated as the most important modality for treating mTBI (median importance of 7). There was a statistically insignificant variation between the specialties as to the importance they place on each of these components. In this Australian tertiary trauma center, there appears to be variation in how clinicians approach mTBI. This study is underpowered to state whether this is between clinicians within a specialty or a trend between specialties. This variation is worthwhile in investigating as a step toward a unified approach to diagnosing, prognosticating, and treating this common pathology.

Keywords: mild traumatic brain injury, adult, clinician, survey

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Authors: Sara Ataei, Maryam Taghizadeh-Ghehi, Amir Sarayani, Asieh Ashouri, Amirhossein Moslehi, Molouk Hadjibabaie, Kheirollah Gholami

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Background: Acute kidney injury (AKI) is common in hematopoietic stem cell transplantation (HSCT) patients with an incidence of 21–73%. Prevention and early diagnosis reduces the frequency and severity of this complication. Predictive biomarkers are of major importance to timely diagnosis. Neutrophil gelatinase associated lipocalin (NGAL) is a widely investigated novel biomarker for early diagnosis of AKI. However, no study assessed NGAL for AKI diagnosis in HSCT patients. Methods: We performed further analyses on gathered data from our recent trial to evaluate the performance of urine NGAL (uNGAL) as an indicator of AKI in 72 allogeneic HSCT patients. AKI diagnosis and severity were assessed using Risk–Injury–Failure–Loss–End-stage renal disease and AKI Network criteria. We assessed uNGAL on days -6, -3, +3, +9 and +15. Results: Time-dependent Cox regression analysis revealed a statistically significant relationship between uNGAL and AKI occurrence. (HR=1.04 (1.008-1.07), P=0.01). There was a relation between uNGAL day +9 to baseline ratio and incidence of AKI (unadjusted HR=.1.047(1.012-1.083), P<0.01). The area under the receiver-operating characteristic curve for day +9 to baseline ratio was 0.86 (0.74-0.99, P<0.01) and a cut-off value of 2.62 was 85% sensitive and 83% specific in predicting AKI. Conclusions: Our results indicated that increase in uNGAL augmented the risk of AKI and the changes of day +9 uNGAL concentrations from baseline could be of value for predicting AKI in HSCT patients. Additionally uNGAL changes preceded serum creatinine rises by nearly 2 days.

Keywords: acute kidney injury, hemtopoietic stem cell transplantation, neutrophil gelatinase-associated lipocalin, Receiver-operating characteristic curve

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Authors: Jia-Shiun Chen, Quoc-Viet Huynh

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This work develops a flexible-body dynamic model of an all-terrain vehicle (ATV), capable of recovering dynamic stresses while the ATV travels on random bumpy roads. The fatigue life of components is forecasted as well. While considering the interaction between dynamic forces and structure deformation, the proposed model achieves a highly accurate structure stress prediction and fatigue life prediction. During the simulation, stress time history of the ATV structure is retrieved for life prediction. Finally, the hot sports of the ATV frame are located, and the frame life for combined road conditions is forecasted, i.e. 25833.6 hr. If the usage of vehicle is eight hours daily, the total vehicle frame life is 8.847 years. Moreover, the reaction force and deformation due to the dynamic motion can be described more accurately by using flexible body dynamics than by using rigid-body dynamics. Based on recommendations made in the product design stage before mass production, the proposed model can significantly lower development and testing costs.

Keywords: flexible-body dynamics, veicle, dynamics, fatigue, durability

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Authors: Sarah A. Harris, Fleur L. McIntyre, Paola T. Chivers, Benjamin G. Piggott, Fiona H. Farringdon

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Australian Rules Football (ARF) is an invasion based, contact field sport with over one million participants. The contact nature of the game increases exposure to all injuries, including head trauma. Evidence suggests that both concussion and sub-concussive traumas such as head knocks may damage the brain, in particular the prefrontal cortex. The prefrontal cortex may not reach full maturity until a person is in their early twenties with males taking longer to mature than females. Repeated trauma to the pre-frontal cortex during maturation may lead to negative social, cognitive and emotional effects. It is also during this period that males exhibit high levels of risk taking behaviours. Risk propensity and the incidence of injury is an unexplored area of research. Little research has considered if the level of player’s (especially younger players) risk propensity in everyday life places them at an increased risk of injury. Hence the current study, investigated if a relationship exists between risk propensity and self-reported injuries including diagnosed concussion and head knocks, among male ARF players aged 18 to 31 years. Method: The study was conducted over 22 weeks with one West Australian Football League (WAFL) club during the 2015 competition. Pre-season risk propensity was measured using the 7-item self-report Risk Propensity Scale. Possible scores ranged from 9 to 63, with higher scores indicating higher risk propensity. Players reported their self-perceived injuries (concussion, head knocks, upper body and lower body injuries) fortnightly using the WAFL Injury Report Survey (WIRS). A unique ID code was used to ensure player anonymity, which also enabled linkage of survey responses and injury data tracking over the season. A General Linear Model (GLM) was used to analyse whether there was a relationship between risk propensity score and total number of injuries for each injury type. Results: Seventy one players (N=71) with an age range of 18.40 to 30.48 years and a mean age of 21.92 years (±2.96 years) participated in the study. Player’s mean risk propensity score was 32.73, SD ±8.38. Four hundred and ninety five (495) injuries were reported. The most frequently reported injury was head knocks representing 39.19% of total reported injuries. The GLM identified a significant relationship between risk propensity and head knocks (F=4.17, p=.046). No other injury types were significantly related to risk propensity. Discussion: A positive relationship between risk propensity and head trauma in contact sports (specifically WAFL) was discovered. Assessing player’s risk propensity therefore, may identify those more at risk of head injuries. Potentially leading to greater monitoring and education of these players throughout the season, regarding self-identification of head knocks and symptoms that may indicate trauma to the brain. This is important because many players involved in WAFL are in their late teens or early 20’s hence, may be at greater risk of negative outcomes if they experience repeated head trauma. Continued education and research into the risks associated with head injuries has the potential to improve player well-being.

Keywords: football, head injuries, injury identification, risk

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Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim, Rashidah Ghazali, Noramli Abdul Razak

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Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of prediction models has facilitated the estimation process in recent years. In this study, 110 crude palm oil (CPO) samples were used to build a free fatty acid (FFA) prediction model. 60% of the collected data were used for training purposes and the remaining 40% used for testing. The visible peaks on the NIR spectrum were at 1725 nm and 1760 nm, indicating the existence of the first overtone of C-H bands. Principal component regression (PCR) was applied to the data in order to build this mathematical prediction model. The optimal number of principal components was 10. The results showed R2=0.7147 for the training set and R2=0.6404 for the testing set.

Keywords: palm oil, fatty acid, NIRS, regression

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Osung Im, Neha Yadav, Eui Hoon Lee, Joong Hoon Kim

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Artificial Neural Network (ANN) approach is commonly used in lots of fields for forecasting. In water resources engineering, forecast of water level or inflow of reservoir is useful for various kind of purposes. Due to advantages of ANN, many papers were written for inflow prediction in river networks, but in this study, ANN is used in urban sewer networks. The growth of severe rain storm in Korea has increased flood damage severely, and the precipitation distribution is getting more erratic. Therefore, effective pump operation in pump station is an essential task for the reduction in urban area. If real time inflow of pump station reservoir can be predicted, it is possible to operate pump effectively for reducing the flood damage. This study used ANN model for pump station reservoir inflow prediction using upstream sewer depth data. For this study, rainfall events, sewer depth, and inflow into Banpo pump station reservoir between years of 2013-2014 were considered. Feed – Forward Back Propagation (FFBF), Cascade – Forward Back Propagation (CFBP), Elman Back Propagation (EBP) and Nonlinear Autoregressive Exogenous (NARX) were used as ANN model for prediction. A comparison of results with ANN model suggests that ANN is a powerful tool for inflow prediction using the sewer depth data.

Keywords: artificial neural network, forecasting, reservoir inflow, sewer depth

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Authors: Ayat E. Ali, Christeen R. Aziz, Merna A. Helmy, Mohammed M. Malek, Sherif H. El-Gohary

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Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.

Keywords: k-nearest neighbor (knn), face detection, vitiligo, bone deformity

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Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Triphonia Ngailo

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Rainfall is a major climatic parameter affecting many sectors such as health, agriculture and water resources. Its quantitative prediction remains a challenge to weather forecasters although numerical weather prediction models are increasingly being used for rainfall prediction. The performance of six convective parameterization schemes, namely the Kain-Fritsch scheme, the Betts-Miller-Janjic scheme, the Grell-Deveny scheme, the Grell-3D scheme, the Grell-Fretas scheme, the New Tiedke scheme of the weather research and forecast (WRF) model regarding quantitative rainfall prediction over Uganda is investigated using the root mean square error for the March-May (MAM) 2013 season. The MAM 2013 seasonal rainfall amount ranged from 200 mm to 900 mm over Uganda with northern region receiving comparatively lower rainfall amount (200–500 mm); western Uganda (270–550 mm); eastern Uganda (400–900 mm) and the lake Victoria basin (400–650 mm). A spatial variation in simulated rainfall amount by different convective parameterization schemes was noted with the Kain-Fritsch scheme over estimating the rainfall amount over northern Uganda (300–750 mm) but also presented comparable rainfall amounts over the eastern Uganda (400–900 mm). The Betts-Miller-Janjic, the Grell-Deveny, and the Grell-3D underestimated the rainfall amount over most parts of the country especially the eastern region (300–600 mm). The Grell-Fretas captured rainfall amount over the northern region (250–450 mm) but also underestimated rainfall over the lake Victoria Basin (150–300 mm) while the New Tiedke generally underestimated rainfall amount over many areas of Uganda. For deterministic rainfall prediction, the Grell-Fretas is recommended for rainfall prediction over northern Uganda while the Kain-Fritsch scheme is recommended over eastern region.

Keywords: convective parameterization schemes, March-May 2013 rainfall season, spatial variation of parameterization schemes over Uganda, WRF model

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Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

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Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

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Authors: Cassandra Maffei, Effie Pomaki, Salomé Deslauriers-Brouillard, Levana Dahan, Caroline Storr, Ramasubramanian Ponnusamy, Philippe S. Archambault

Abstract:

Sexual health education following spinal cord injury (SCI) remains poorly integrated into the rehabilitation process, especially in low-income countries where the topics of disability and sexuality are stigmatized. This research aimed to evaluate a sexual health manual that was created and distributed amongst males with SCI who had received rehabilitation services at Amar Seva Sangam (ASSA), a rehabilitation center located in rural South India. A service evaluation was completed to collect data from a convenience sample of 37 males with spinal cord injuries. Data were analyzed using descriptive statistics and content analysis. The service evaluation showed that the manual was well received by the sample and had positive impacts on secondary outcome measures, including relationship dynamics and quality of life. It can thus be used as an effective adjunct tool to support the improvement of sexual health knowledge, attitudes, and practices of individuals with SCI.

Keywords: spinal cord injury, sexual health, rehabilitation, India, education, service evaluation

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Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

Abstract:

This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

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Authors: Merrick Lopez, Aashish Abraham, Melissa Egge, Marissa Hood, Jui Shah

Abstract:

A 3 year old male with unknown medical history presented initially with encephalopathy, intubated for respiratory failure, and admitted to the pediatric intensive care unit (PICU) with refractory shock. During resuscitation in the emergency department, he was found to be in severe metabolic acidosis with a pH of 7.03 and escalated on vasopressor drips for hypotension. His initial sodium was 174. He was noted to have burn injuries to his scalp, forehead, right axilla, bilateral arm creases and lower legs. He had rhabdomyolysis (initial creatinine kinase 5,430 U/L with peak levels of 62,340 normal <335 U/L), cardiac injury (initial troponin 88 ng/L with peak at 145 ng/L, normal <15ng/L), hypernatremia (peak 174, normal 140), hypocalcemia, liver injury, acute kidney injury, and neuronal loss on magnetic resonance imaging (MRI). Soft restraints and a shock collar were found in the home. He was critically ill for 8 days, but was gradually weaned off drips, extubated, and started on feeds. Discussion Electrical injury, specifically lightning injury is an uncommon but devastating cause of injury in pediatric patients. This patient with suspected abusive use of a dog shock collar presented similar to a lightning strike. Common entrance points include the hands and head, similar to our patient with linear wounds on his forehead. When current enters, it passes through tissues with the least resistance. Nerves, blood vessels, and muscles, have high fluid and electrolyte content and are commonly affected. Exit points are extremities: our child who had circumferential burns around his arm creases and ankles. Linear burns preferentially follow areas of high sweat concentration, and are thought to be due to vaporization of water on the skin’s surface. The most common cause of death from a lightning strike is due to cardiopulmonary arrest. The massive depolarization of the myocardium can result in arrhythmias and myocardial necrosis. The patient presented in cardiogenic shock with evident cardiac damage. Electricity going through vessels can lead to vaporization of intravascular water. This can explain his severe hypernatremia. He also sustained other internal organ injuries (adrenal glands, pancreas, liver, and kidney). Electrical discharge also leads to direct skeletal muscle injury in addition to prolonged muscular spasm. Rhabdomyolysis, the acute damage of muscle, leads to release of potentially toxic components into the circulation which could lead to acute renal failure. The patient had severe rhabdomyolysis and renal injury. Early hypocalcemia has been consistently demonstrated in patients with rhabdomyolysis. This was present in the patient and led to increased vasopressor needs. Central nervous system injuries are also common which can include encephalopathy, hypoxic injury, and cerebral infarction. The patient had evidence of brain injury as seen on MRI. Conclusion Electrical injuries due to lightning strikes and abusive use of a dog shock collar are rare, but can both present in similar ways with respiratory failure, shock, hypernatremia, rhabdomyolysis, brain injury, and multiorgan damage. Although rare, it is essential for early identification and prompt management for acute and chronic complications in these children.

Keywords: cardiogenic shock, dog shock collar, lightning strike, rhabdomyolysis

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Authors: Salman Mohamadi, Seyed Mohammad Ali Tayaranian Hosseini, Hamidreza Amindavar

Abstract:

In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes.

Keywords: epileptic seizure prediction , ARIMA, ARCH and GARCH modeling, heteroskedasticity, EEG

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Authors: Thu Nguyen, Jane Daugherty-Luck

Abstract:

Falx subdural hematoma is a life-threatening condition associated with high mortality. We present a patient case who had fallen with no head injury or loss of conspicuousness. She had tenderness along cervical and thoracic lumbar spine. CT head revealed falx subdural hematoma. The patient was managed medically. The pathophysiology of falx subdural hematoma is linked to laceration of bridging veins provoked by frontal or occipital impact. Posttraumatic subdural hematoma is commonly caused by inertia instead of facture or cerebral contusion resulting from direct impact. The theory is consistent with the lack of fracture in most cases in the literature. Our patient had neither contusion nor fracture.

Keywords: falx subdural hematoma, traumatic head injury, CT head scan, bridging veins, inertia

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Authors: Tuji Mohammed Sani

Abstract:

Back ground: An open tibial fracture is an injury where the fractured bone directly communicates with the outside environment. Due to the specific anatomical features of the tibia (limited soft tissue coverage), more than quarter of its fractures are classified as open, representing the most common open long-bone injuries. Open tibial fractures frequently cause significant bone comminution, periosteal stripping, soft tissue loss, contamination and are prone to bacterial entry with biofilm formation, which increases the risk of deep bone infection. Objective: The main objective of the study was to determine Prevalence of infection and its associated factors in surgically treated open tibial fracture in Addis Ababa Burn Emergency and Trauma (AaBET) center. Method: A facility based retrospective cross-sectional study was conducted among patient treated for open tibial fracture at AaBET center from September 2018 to September 2021. The data was collected from patient’s chart using structured data collection form, and Data was entered and analyzed using SPSS version 26. Bivariable and multiple binary logistic regression were fitted. Multicollinearity was checked among candidate variables using variance inflation factor and tolerance, which were less than 5 and greater than 0.2, respectively. Model adequacy were tested using Hosmer-Lemeshow goodness of fitness test (P=0.711). AOR at 95% CI was reported, and P-value < 0.05 was considered statistically significant. Result: This study found that 33.9% of the study participants had an infection. Initial IV antibiotic time (AOR=2.924, 95% CI:1.160- 7.370) and time of wound closure from injury (AOR=3.524, 95% CI: 1.798-6.908), injury to admission time (AOR=2.895, 95% CI: 1.402 – 5.977). and definitive fixation method (AOR=0.244, 95% CI: 0.113 – 0.4508) were the factors found to have a statistically significant association with the occurrence of infection. Conclusion: The rate of infection in open tibial fractures indicates that there is a need to improve the management of open tibial fracture treated at AaBET center. Time from injury to admission, time from injury to first debridement, wound closure time, and initial Intra Venous antibiotic time from the injury are an important factor that can be readily amended to improve the infection rate. Whether wound closed before seven days or not were more important factor associated with occurrences of infection.

Keywords: infection, open tibia, fracture, magnitude

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Authors: Xiao-Yin Liu, Liang-Xue Zhou

Abstract:

Traumatic brain injury (TBI), as a kind of nerve trauma caused by an external force, affects people all over the world and is a global public health problem. Although there are various clinical treatments for brain injury, including surgery, drug therapy, and rehabilitation therapy, the therapeutic effect is very limited. To improve the therapeutic effect of TBI, scaffolds combined with exosomes are a promising but challenging method for TBI repair. In this study, we examined whether a novel 3D-printed collagen/chitosan scaffold/exosomes derived from neural stem cells (NSCs) pretreated with insulin growth factor-1 (IGF-I) scaffolds (3D-CC-INExos) could be used to improve TBI repair and functional recovery after TBI. Our results showed that composite scaffolds of collagen-, chitosan- and exosomes derived from NSCs pretreated with IGF-I (INExos) could continuously release the exosomes for two weeks. In the rat TBI model, 3D-CC-INExos scaffold transplantation significantly improved motor and cognitive function after TBI, as assessed by the Morris water maze test and modified neurological severity scores. In addition, immunofluorescence staining and transmission electron microscopy showed that the recovery of damaged nerve tissue in the injured area was significantly improved by 3D-CC-INExos implantation. In conclusion, our data suggest that 3D-CC-INExos might provide a potential strategy for the treatment of TBI and lay a solid foundation for clinical translation.

Keywords: traumatic brain injury, exosomes, insulin growth factor-1, neural stem cells, collagen, chitosan, 3D printing, neural regeneration, angiogenesis, functional recovery

Procedia PDF Downloads 82