Search results for: mortality prediction

Authors: Mostafa Najim, Alaa Rahhal, Fadi Khir, Safae Abu Yousef, Amer Aljundi, Feryal Ibrahim, Aliaa Amer, Ahmed Soliman Mohamed, Samira Saleh, Dekra Alfaridi, Ahmed Mahfouz, Sumaya Al-Yafei, Faraj Howady, Mohamad Yahya Khatib, Samar Alemadi

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Background: Coronavirus disease 2019 (COVID-19) increases the risk of coagulopathy among critically ill patients. Although the presence of antiphospholipid antibodies (aPLs) has been proposed as a possible mechanism of COVID-19 induced coagulopathy, their clinical significance among critically ill patients with COVID-19 remains uncertain. Methods: This prospective observational study included patients with COVID-19 admitted to intensive care units (ICU) to evaluate the prevalence and clinical significance of aPLs, including anticardiolipin IgG/IgM, anti-β2-glycoprotein IgG/IgM, and lupus anticoagulant. The study outcomes included the prevalence of aPLs, a primary composite outcome of all-cause mortality, and arterial or venous thrombosis among aPLs positive patients versus aPLs negative patients during their ICU stay. Multiple logistic regression was used to assess the influence of aPLs on the primary composite outcome of mortality and thrombosis. Results: A total of 60 critically ill patients were enrolled. Of whom, 57 (95%) were male, with a mean age of 52.8 ± 12.2 years, and the majority were from Asia (68%). Twenty-two patients (37%) were found to have positive aPLs; of whom 21 patients were positive for lupus anticoagulant, whereas one patient was positive for anti-β2-glycoprotein IgG/IgM. The composite outcome of mortality and thrombosis during ICU did not differ among patients with positive aPLs compared to those with negative aPLs (4 (18%) vs. 6 (16%), aOR= 0.98, 95% CI 0.1-6.7; p-value= 0.986). Likewise, the secondary outcomes, including all-cause mortality, venous thrombosis, arterial thrombosis, discharge from ICU, time to mortality, and time to discharge from ICU, did not differ between those with positive aPLs upon ICU admission in comparison to patients with negative aPLs. Conclusion: The presence of aPLs does not seem to affect the outcomes of critically ill patients with COVID-19 in terms of all-cause mortality and thrombosis. Therefore, clinicians may not screen critically ill patients with COVID-19 for aPLs unless deemed clinically appropriate.

Keywords: antiphospholipid antibodies, critically ill patients, coagulopathy, coronavirus

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Authors: Kiyoumars Roushangar, Saeid Sadaghian

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Although accurate sediment load prediction is very important in planning, designing, operating and maintenance of water resources structures, the transport mechanism is complex, and the deterministic transport models are based on simplifying assumptions often lead to large prediction errors. In this research, firstly, two intelligent ANN methods, Radial Basis and General Regression Neural Networks, are adopted to model of total sediment load transport into Madani Dam reservoir (north of Iran) using the measured data and then applicability of the sediment transport methods developed by Engelund and Hansen, Ackers and White, Yang, and Toffaleti for predicting of sediment load discharge are evaluated. Based on comparison of the results, it is found that the GRNN model gives better estimates than the sediment rating curve and mentioned classic methods.

Keywords: sediment transport, dam reservoir, RBF, GRNN, prediction

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Danladi Ali, Abdullahi Mukaila

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In this work, GSM signal strength (power) was monitored in an indoor environment. Samples of the GSM signal strength was measured on mobile equipment (ME). One-dimensional multilevel wavelet is used to predict the fading phenomenon of the GSM signal measured and neural network clustering to determine the average power received in the study area. The wavelet prediction revealed that the GSM signal is attenuated due to the fast fading phenomenon which fades about 7 times faster than the radio wavelength while the neural network clustering determined that -75dBm appeared more frequently followed by -85dBm. The work revealed that significant part of the signal measured is dominated by weak signal and the signal followed more of Rayleigh than Gaussian distribution. This confirmed the wavelet prediction.

Keywords: decomposition, clustering, propagation, model, wavelet, signal strength and spectral efficiency

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Authors: Ciro Cursio, Giulia Cursio, Pio Luigi Cursio, Luigi Cursio

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Communication between practitioner and patient is of the utmost importance in aesthetic medicine: as of today, images of previous treatments are the most common tool used by doctors to describe and anticipate future results for their patients. However, using photos of other people often reduces the engagement of the prospective patient and is further limited by the number and quality of pictures available to the practitioner. Pre-existing work solves this issue in two ways: 3D scanning of the area with manual editing of the 3D model by the doctor or automatic prediction of the treatment by warping the image with hand-written parameters. The first approach requires the manual intervention of the doctor, while the second approach always generates results that aren’t always realistic. Thus, in one case, there is significant manual work required by the doctor, and in the other case, the prediction looks artificial. We propose an AI-based algorithm that autonomously generates a realistic prediction of treatment results. For the purpose of this study, we focus on hyaluronic acid treatments in the facial area. Our approach takes into account the individual characteristics of each face, and furthermore, the prediction system allows the patient to decide which area of the face she wants to modify. We show that the predictions generated by our system are realistic: first, the quality of the generated images is on par with real images; second, the prediction matches the actual results obtained after the treatment is completed. In conclusion, the proposed approach provides a valid tool for doctors to show patients what they will look like before deciding on the treatment.

Keywords: prediction, hyaluronic acid, treatment, artificial intelligence

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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: 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: Emna Ketata, Wafa Farhat

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Introduction: Hyponatremia is defined by a blood sodium level of ≤ 136 mmol/L; it is associated with a high risk of morbidity and mortality in the emergency room. This was explained by transit disorders, including diarrhea and inappropriate antidiuretic hormone secretion (Syndrome of inappropriate antidiuretic hormone secretion). Pneumonia can cause dyspnea, stress-causing SIADH and digestive symptoms (diarrhea and vomiting). Aim: The purpose of this study was to determine the link between pneumonia and hyponatremia as a predictor of patient’s prognosis and intra-hospital mortality. Methodology: This is a prospective observational study over a period of 3 years in the emergency department. Inclusion :patients (age > 14 years), with clinical signs in favor of pneumonia. Natremia was measured. Natremia was classified as mild to moderate with a blood sodium level between 121 and 135 mmol/L and as severe with a blood sodium level ≤ 120 mmol/L. Results: This study showed an average serum sodium value of 135 mmol/L (range 114–159 mmol/L) in these patients. Hyponatremia was observed in 123 patients (43.6%), 115 patients (97,8%) had mild to moderate hyponatremia and 2,8% had severe hyponatremia. The mean age was 65±17 years with a sex ratio of 1.05. The main reason for consultation in patients with hyponatremia was cough in 58 patients (47.2%), and digestive symptoms were present in 25 patients (20.3. An altered state of consciousness was observed in 11 patients (3%). Patients with hyponatremia had greater heart rate (p=0.02),white blood cell count (p=0.009) , plasmatic lactate (p=0.002) and higher rate of pneumonia recurrence (p=0.001) .In addition, 80% of them have a positive CURB65 score (>=2). hyponatremia had higher rates of use of oxygen therapy compared to patients with normo-natremia (54% vs. 45%). The analytical study showed that hyponatremia is significantly associated with intra-hospital mortality with( p=0.01), severe hyponatremia p=0.04. Conclusion: Hyponatremia is a predictor of mortality and worse prognosis. Recognition of the pathophysiological mechanisms of hyponatremia in pneumonia will probably allow better management of it.

Keywords: oxygenotherapy, mortality, recurrence, positif curb65

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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: J. Aliyu, A. O. Raji, A. A. Ibrahim

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The study was carried out to assess the fertility, early and late embryonic mortalities as well as hatchability by strain, season and hen’s weight in Nigerian indigenous chickens reared on deep litter. Four strains (normal feathered, naked neck, frizzle and dwarf) of hens maintained at a mating ratio of 1 cock to 4 hens, fed breeders mash and water ad libitum were used in a three year experiment. The data generated were subjected to analysis of variance using the SAS package and the means, where significant, were separated using the least significant difference (LSD). There were significant effects (P < 0.05) of strain on all the traits studied. Fertility was generally high (84.29 %) in all the strains. Early embryonic mortality was significantly lowest (P < 0.01) in naked neck which had the highest late embryonic mortality (P < 0.001). Hatchability was significantly highest (P < 0.01) in normal feathered (80.23 %) and slightly depressed in frizzle (74.95 %) and dwarf (72.27 %) while naked neck had the lowest (60.80 %). Season of the year had significant effects on early embryonic mortality. Dry hot season significantly (P < 0.05) depressed fertility while early embryonic mortality was depressed in the wet season (15.33 %). Early and late embryonic mortalities significantly increased (P < 0.05) with increasing weight of hen. Dwarf, frizzle and normal feathered hens could be used to improve hatchability as well as reduce early and late embryonic mortalities in Nigerian indigenous chickens.

Keywords: chicken, fertility, hatchability, indigenous, strain

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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: 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: Elizabeth Behrens, Anne Timmermann, Alexander Yerkan, Joshua Thomas, Deborah Katz, Agne Paner, Melissa Larson, Shivi Jain, Seo-Hyun Kim, Celalettin Ustun, Ankur Varma, Parameswaran Venugopal, Jamile Shammo

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Coronavirus Disease-2019 (COVID-19) causes persistent concern for poor outcomes in vulnerable populations. Patients with hematologic malignancies (HM) have been found to have higher COVID-19 case fatality rates compared to those without malignancy. While cytopenias are common in patients with HM, especially in those undergoing chemotherapy treatment, hemoglobin (Hgb) and platelet count have not yet been studied, to our best knowledge, as potential prognostic indicators for patients with HM and COVID-19. The goal of this study is to identify factors that may increase the risk of mortality in patients with HM and COVID-19. In this single-center, retrospective, observational study, 65 patients with HM and laboratory confirmed COVID-19 were identified between March 2020 and January 2021. Information on demographics, laboratory data the day of COVID-19 diagnosis, and prognosis was extracted from the electronic medical record (EMR), chart reviewed, and analyzed using the statistical software SAS version 9.4. Chi-square testing was used for categorical variable analyses. Risk factors associated with mortality were established by logistic regression models. Non-Hodgkin lymphoma (37%), chronic lymphocytic leukemia (20%), and plasma cell dyscrasia (15%) were the most common HM. Higher Hgb level upon COVID-19 diagnosis was related to decreased mortality, odd ratio=0.704 (95% confidence interval [CI]: 0.511-0.969; P = .0263). Platelet count the day of COVID-19 diagnosis was lower in patients who ultimately died (mean 127 ± 72K/uL, n=10) compared to patients who survived (mean 197 ±92K/uL, n=55) (P=.0258). Female sex was related to decreased mortality, odd ratio=0.143 (95% confidence interval [CI]: 0.026-0.785; P = .0353). There was no mortality difference between the patients who were on treatment for HM the day of COVID-19 diagnosis compared to those who were not (P=1.000). Lower Hgb and male sex are independent risk factors associated with increased mortality of HM patients with COVID-19. Clinicians should be especially attentive to patients with HM and COVID-19 who present with cytopenias. Larger multi-center studies are urgently needed to further investigate the impact of anemia, thrombocytopenia, and demographics on outcomes of patients with hematologic malignancies diagnosed with COVID-19.

Keywords: anemia, COVID-19, hematologic malignancy, prognosis

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Authors: Chee Dhang Chen, Hiroyuki Takaoka, Koon Weng Lau, Poh Ruey Tan, Ai Chdon Chin, Van Lun Low, Abdul Aziz Azidah, Mohd Sofian-Azirun

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Susceptibility status of Simulium (Simulium) nobile (Diptera: Simuliidae) adults obtained from Pahang, Malaysia was evaluated against 11 adulticides representing four major insecticide classes: organochlorines (DDT, dieldrin), organophosphates (malathion, fenitrothion), carbamates (bendiocarb, propoxur) and pyrethroids (etofenprox, deltamethrin, lambdacyhalothrin, permethrin, cyfluthrin). The adult bioassay was conducted according to WHO standard protocol to determine the insecticide susceptibility. Mortality at 24 h post treatment was used as indicator for susceptibility status. The results revealed that S. nobile obtained was susceptible to propoxur, cyfluthrin and bendiocarb with 100% mortality. S. nobile was resistant or exhibited some tolerant against lambdacyhalothrin and deltamethrin with mortality ranged ≥ 90% but < 98%. S. nobile populations in Pahang exhibited different level of resistant against 11 adulticides with mortality ranged from 60.00 ± 10.00 to 100.00 ± 0.00. In conclusion, S. nobile populations in Pahang were susceptible to propoxur, cyfluthrin and bendiocarb. The susceptibility status of S. nobile in descending order was propoxur, cyfluthrin > bendicarb > deltamethrin > lambdacyhalothrin > permethrin > etofenprox > DDT > malathion > fenitrothion > dieldrin. Regular surveys should be conducted to monitor the susceptibility status of this insect vector in order to prevent further development of resistance.

Keywords: black fly, adult bioassay, insecticide resistance, Malaysia

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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: 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: Josine Beek, S. Agten, R. Del Pozo, B. Balis

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The objective of the present study was to compare the safety of intramuscular administration of Zuprevo® (tildipirosin, 40 mg/mL) with Draxxin® (tulathromycin, 100 mg/mL) in the treatment of swine respiratory disease at weaning age. The trial was carried out in two farrow-to-finish farms with 300 sows (farm A) and 500 sows (farm B) in a batch-production system. Farm A had no history of respiratory problems, whereas farm B had a history of respiratory outbreaks with increased mortality ( > 2%) in the nursery. Both farms were positive to Pasteurella multocida, Bordetella bronchiseptica, Actinobacillus pleuropneumoniae and Haemophilus parasuis. From each farm, one batch of piglets was included (farm A: 644 piglets; farm B: 963 piglets). One day before weaning (day 0; 18-21 days of age), piglets were identified by an individual ear tag and randomly assigned to a treatment group. At day 0, Group 1 was treated with a single intramuscular injection with Zuprevo® (tildipirosin, 40 mg/mL; 1 mL/10 kg) and group 2 with Draxxin® (tulathromycin, 100 mg/mL; 1 mL/40 kg). For practical reasons, dosage of the product was adjusted according to three weight categories: < 4 kg, 4-6 kg and > 6 kg. Within each farm, piglets of both groups were comingled at weaning and subsequently managed and located in the same facilities and with identical environmental conditions. Our study involved the period from day 0 until 10 weeks of age. Safety of treatment was evaluated by 1) visual examination for signs of discomfort directly after treatment and after 15 min, 1 h and 24 h and 2) mortality rate within 24 h after treatment. Efficacy of treatment was evaluated based on mortality rate from day 0 until 10 weeks of age. Each piglet that died during the study period was necropsied by the herd veterinarian to determine the probable cause of death. Data were analyzed using binary logistic regression and differences were considered significant if p < 0.05. The pig was the experimental unit. In total, 848 piglets were treated with tildipirosin and 759 piglets with tulathromycin. In farm A, one piglet with retarded growth ( < 1 kg at 18 days of age) showed an adverse reaction after injection of tildipirosin: lateral recumbence and dullness for ± 30 sec. The piglet recovered after 1-2 min. This adverse reaction was probably due to overdosing (12 mg/kg). No adverse effect of treatment was observed in any other piglet. There was no mortality within 24 h after treatment. No significant difference was found in mortality rate between both groups from day 0 until 10 weeks of age. In farm A, overall mortality rate was 0.3% (2/644). In farm B, mortality rate was 0.2% (1/502) in group 1 (tildipirosin) and 0.9% (4/461) in group 2 (tulathromycin)(p=0.60). The necropsy of piglets that died during the study period revealed no macroscopic lesions of the respiratory tract. In conclusion, Zuprevo® (tildipirosin, 40 mg/mL) was shown to be a safe and efficacious alternative to Draxxin® (tulathromycin, 100 mg/mL) for the early treatment of swine respiratory disease at weaning age.

Keywords: antibiotic treatment, safety, swine respiratory disease, tildipirosin

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Authors: T. Pradhan, P. Rijal, M. C. Regmi

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Background: Eclampsia is a multisystem disorder that involves vital organs and failure of these may lead to deterioration of maternal condition and hypoxia and acidosis of fetus resulting in high maternal and perinatal mortality and morbidity. Thus, evaluation of the contributing factors for this condition and its complications leading to maternal deaths should be the priority. Formulating the plan and protocol to decrease these losses should be our goal. Aims and Objectives: To evaluate the risk factors associated with adverse maternal and fetal outcome in patients with eclampsia and to correlate the risk factors associated with maternal and fetal morbidity and mortality. Methods: All patients with eclampsia admitted in Department of Obstetrics and Gynecology, B. P. Koirala Institute of Health Sciences were enrolled after informed consent from February 2013 to February 2014. Questions as per per-forma were asked to patients, and attendants like Antenatal clinic visits, parity, number of episodes of seizures, duration from onset of seizure to magnesium sulfate and the patients were followed as per the hospital protocol, the mode of delivery, outcome of baby, post partum maternal condition like maternal Intensive Care Unit admission, neurological impairment and mortality were noted before discharge. Statistical analysis was done using Statistical Package for the Social Sciences (SPSS 11). Mean and percentage were calculated for demographic variables. Pearson’s correlation test and chi-square test were applied to find the relation between the risk factors and the outcomes. P value less than 0.05 was considered significant. Results: There were 10,000 antenatal deliveries during the study period. Fifty-two patients with eclampsia were admitted. All of the patients were unbooked for our institute. Thirty-nine patients were antepartum eclampsia. Thirty-one patients required mechanical ventilator support. Twenty-four patients were delivered by emergency c-section and 21 babies were Low Birth Weight and there were 9 stillbirths. There was one maternal mortality and 45 patients were discharged with improvement but 3 patients had neurological impairment. Mortality was significantly related with number of seizure episodes and time interval between seizure onset and administration of magnesium sulphate. Conclusion: Early detection and management of hypertensive complicating pregnancy during antenatal clinic check up. Early hospitalization and management with magnesium sulphate for eclampsia can help to minimize the maternal and fetal adverse outcomes.

Keywords: eclampsia, maternal mortality, perinatal mortality, risk factors

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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: Christos Lampropoulos, Maria Konsta, Ifigenia Apostolou, Vicky Dradaki, Tamta Sirbilatze, Irini Dri, Christina Kordali, Vaggelis Lambas, Kostas Argyros, Georgios Mavras

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Objectives: Mediterranean diet has been associated with lower incidence of cardiovascular disease and cancer. The purpose of our study was to examine the hypothesis that Mediterranean diet may protect against mortality and reduce admission duration in elderly, hospitalized patients. Methods: Sample population included 150 patients (78 men, 72 women, mean age 80±8.2). The following data were taken into account in analysis: anthropometric and laboratory data, dietary habits (MedDiet score), patients’ nutritional status [Mini Nutritional Assessment (MNA) score], physical activity (International Physical Activity Questionnaires, IPAQ), smoking status, cause and duration of current admission, medical history (co-morbidities, previous admissions). Primary endpoints were mortality (from admission until 6 months afterwards) and duration of admission, compared to national guidelines for closed consolidated medical expenses. Logistic regression and linear regression analysis were performed in order to identify independent predictors for mortality and admission duration difference respectively. Results: According to MNA, nutrition was normal in 54/150 (36%) of patients, 46/150 (30.7%) of them were at risk of malnutrition and the rest 50/150 (33.3%) were malnourished. After performing multivariate logistic regression analysis we found that the odds of death decreased 30% per each unit increase of MedDiet score (OR=0.7, 95% CI:0.6-0.8, p < 0.0001). Patients with cancer-related admission were 37.7 times more likely to die, compared to those with infection (OR=37.7, 95% CI:4.4-325, p=0.001). According to multivariate linear regression analysis, admission duration was inversely related to Mediterranean diet, since it is decreased 0.18 days on average for each unit increase of MedDiet score (b:-0.18, 95% CI:-0.33 - -0.035, p=0.02). Additionally, the duration of current admission increased on average 0.83 days for each previous hospital admission (b:0.83, 95% CI:0.5-1.16, p<0.0001). The admission duration of patients with cancer was on average 4.5 days higher than the patients who admitted due to infection (b:4.5, 95% CI:0.9-8, p=0.015). Conclusion: Mediterranean diet adequately protects elderly, hospitalized patients against mortality and reduces the duration of hospitalization.

Keywords: Mediterranean diet, malnutrition, nutritional status, prognostic factors for mortality

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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: Mujahid Ali, Wasif Mohammed Ali, Meraj Ahmad

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Background: Perforation peritonitis is the most common surgical emergency encountered by surgeons all over the world as well as in India. The etiology of perforation peritonitis in India continues to be different from its western counterparts. The aim of this study is to evaluate the spectrum of cases of perforation peritonitis at our hospital. Methods: A prospective study conducted includes three hundred thirtysix patients of perforation peritonitis at J. N. Medical College from October 2015 to July 2017. The patients were admitted, resuscitated and underwent emergency laparotomy. Data were collected in terms of demographic profile, clinical presentations, site of perforations, causes and surgical outcomes. Results: In this study, the most common cause of perforation peritonitis was peptic ulcer disease (43%), followed by enteric perforation (12.8%), tubercular perforation (12.5%), traumatic perforation (11.9%), appendicular perforation (9.8%), amoebic caecal perforation (3%), malignant perforation (1.5%), etc. The sites of perforations were stomach in majority (38.3%), ileum (31%), appendix (8%), duodenum (5.%), caecum (4.4%) ,colon (3%), jejunum (8.5%) and gall bladder (2%). The overall mortality was 21% in our study. Age >50 years (p= <0.0001, OR= 3.9260, CI= 2.2 to 6.9), organ failure (p= <0.0001, OR= 29.2, CI= 14.8 to 57.6), shock (p=<0.0001, OR=20.20, CI= 10.56 to 38.6), diffuse peritonitis (p<0.0015, OR= 6.8810, CI= 2.09 to 22.57) and faecal exudates (p<0.0001) were found to be significant factors affecting mortality. The most common complication associated was superficial wound infection (40%), followed by burst abdomen seen in 21% cases, intra-abdominal sepsis in 18% cases, electrolyte imbalances in 15% cases, anastomotic leak in 6% cases. Conclusion: In this study, stomach is the most common site of perforation with peptic ulcer disease being the most common etiology. Older age, presence of shock, organ failure and faecal peritonitis were the risk factors affecting the mortality of the patients. Early recognition, adequate resuscitation and referral of patients can influence outcome and reduces mortality as well as morbidity.

Keywords: etiology, mortality, perforation, spectrum

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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: Vineet Agrawal, Deepanjali S., Medha R., Subitha L.

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Background. Hospital malnutrition is a highly prevalent issue and is known to increase the morbidity, mortality, length of hospital stay, and cost of care. In India, studies on hospital malnutrition have been restricted to ICU, post-surgical, and cancer patients. We designed this study to assess the impact of hospital malnutrition on 30-day post-discharge and in-hospital mortality in patients admitted in the general medicine department, irrespective of diagnosis. Methodology. All patients aged above 18 years admitted in the medicine wards, excluding medico-legal cases, were enrolled in the study. Nutritional assessment was done within 72 h of admission, using Subjective Global Assessment (SGA), which classifies patients into three categories: Severely malnourished, Mildly/moderately malnourished, and Normal/well-nourished. Anthropometric measurements like Body Mass Index (BMI), Triceps skin-fold thickness (TSF), and Mid-upper arm circumference (MUAC) were also performed. Patients were followed-up during hospital stay and 30 days after discharge through telephonic interview, and their final diagnosis, comorbidities, and cause of death were noted. Multivariate logistic regression and cox regression model were used to determine if the nutritional status at admission independently impacted mortality at one month. Results. The prevalence of malnourishment by SGA in our study was 67.3% among 395 hospitalized patients, of which 155 patients (39.2%) were moderately malnourished, and 111 (28.1%) were severely malnourished. Of 395 patients, 61 patients (15.4%) expired, of which 30 died in the hospital, and 31 died within 1 month of discharge from hospital. On univariate analysis, malnourished patients had significantly higher morality (24.3% in 111 Cat C patients) than well-nourished patients (10.1% in 129 Cat A patients), with OR 9.17, p-value 0.007. On multivariate logistic regression, age and higher Charlson Comorbidity Index (CCI) were independently associated with mortality. Higher CCI indicates higher burden of comorbidities on admission, and the CCI in the expired patient group (mean=4.38) was significantly higher than that of the alive cohort (mean=2.85). Though malnutrition significantly contributed to higher mortality on univariate analysis, it was not an independent predictor of outcome on multivariate logistic regression. Length of hospitalisation was also longer in the malnourished group (mean= 9.4 d) compared to the well-nourished group (mean= 8.03 d) with a trend towards significance (p=0.061). None of the anthropometric measurements like BMI, MUAC, or TSF showed any association with mortality or length of hospitalisation. Inference. The results of our study highlight the issue of hospital malnutrition in medicine wards and reiterate that malnutrition contributes significantly to patient outcomes. We found that SGA performs better than anthropometric measurements in assessing under-nutrition. We are of the opinion that the heterogeneity of the study population by diagnosis was probably the primary reason why malnutrition by SGA was not found to be an independent risk factor for mortality. Strategies to identify high-risk patients at admission and treat malnutrition in the hospital and post-discharge are needed.

Keywords: hospitalization outcome, length of hospital stay, mortality, malnutrition, subjective global assessment (SGA)

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