Search results for: mortality prediction

Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model

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Authors: Kuo-Chun Chien, Chia-Hui Chang, Ren-Der Sun

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Legal Judgment Prediction (LJP) is a subtask for legal AI. Its main purpose is to use the facts of a case to predict the judgment result. In Taiwan's criminal procedure, when prosecutors complete the investigation of the case, they will decide whether to prosecute the suspect and which article of criminal law should be used based on the facts and evidence of the case. In this study, we collected 305,240 indictments from the public inquiry system of the procuratorate of the Ministry of Justice, which included 169 charges and 317 articles from 21 laws. We take the crime facts in the indictments as the main input to jointly learn the prediction model for law source, article, and charge simultaneously based on the pre-trained Bert model. For single article cases where the frequency of the charge and article are greater than 50, the prediction performance of law sources, articles, and charges reach 97.66, 92.22, and 60.52 macro-f1, respectively. To understand the big performance gap between articles and charges, we used a bipartite graph to visualize the relationship between the articles and charges, and found that the reason for the poor prediction performance was actually due to the wording precision. Some charges use the simplest words, while others may include the perpetrator or the result to make the charges more specific. For example, Article 284 of the Criminal Law may be indicted as “negligent injury”, "negligent death”, "business injury", "driving business injury", or "non-driving business injury". As another example, Article 10 of the Drug Hazard Control Regulations can be charged as “Drug Control Regulations” or “Drug Hazard Control Regulations”. In order to solve the above problems and more accurately predict the article and charge, we plan to include the article content or charge names in the input, and use the sentence-pair classification method for question-answer problems in the BERT model to improve the performance. We will also consider a sequence-to-sequence approach to charge prediction.

Keywords: legal judgment prediction, deep learning, natural language processing, BERT, data visualization

Procedia PDF Downloads 102

Authors: Maria Lucia G. Lourenco, Keylla H. N. P. Pereira, Vivane Y. Hibaru, Fabiana F. Souza, Joao C. P. Ferreira, Simone B. Chiacchio, Luiz H. A. Machado

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Neonatal sepsis is a systemic response to the acute generalized infection caused by one or more bacterial agents, representing the main infectious cause of neonatal mortality in dogs during the first three weeks of life. This study aims to describe the incidence of sepsis in neonate dogs, as well as the main clinical signs and mortality rates. The study included 735 neonates admitted to the Sao Paulo State University (UNESP) Veterinary Hospital, Botucatu, Sao Paulo, Brazil, between January 2018 and November 2019. Seven hundred thirty-five neonates, 14% (98/703) presented neonatal sepsis. The main sources of infection for the neonates were intrauterine (72.5%, 71/98), lactogenic (13.2%, 13/98), umbilical (5.1%, 5/98) and unidentified sources (9.2%, 9/98). The main non-specific clinical signs observed in the newborns were weakness, depression, impaired or absent reflexes, hypothermia, hypoglycemia, dehydration, reduced muscle tonus and diarrhea. The newborns also manifested clinical signs of severe infection, such as hyperemia in the abdominal and anal regions, omphalitis, hematuria, abdomen and extremities with purplish-blue coloration necrosing injuries in the pads, bradycardia, dyspnea, epistaxis, hypotension and evolution to septic shock. Infections acquired during intrauterine life led to the onset of the clinical signs at the time of birth, with fast evolution during the first hours of life. On the other hand, infections acquired via milk or umbilical cord presented clinical signs later. The total mortality rate was 5.4% (38/703) and the mortality rate among the neonates with sepsis was 38.7% (38/98). The early mortality rate (0 to 2 days) accounted for 86.9% (33/38) and the late mortality rate (3 to 30 days) for 13.1% (5/38) of the deaths among the newborns with sepsis. The main bacterial agents observed were Staphylococcus spp., Streptococcus spp., Proteus spp. Mannheimia spp. and Escherichia coli. Neonatal sepsis evolves quickly and may lead to high mortality in a litter. The prognosis is usually favorable if the diagnosis is reached early and the antibiotic therapy instituted as soon as possible, even before the results of blood cultures and antibiograms. The therapeutic recommendations should meet the special physiological conditions of a neonate in terms of metabolism and excretion of medication. Therefore, it is of utmost importance that the veterinarian is knowledgeable regarding neonatology to provide effective intervention and improve the survival rates of these patients.

Keywords: Neonatal infection , bacteria, puppies, newborn

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Authors: Rupesh K. Chaudhary, Narinder P. Jain, Rajesh Mahajan, Rajat Manchanda

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Introduction: Delirium is a complex, multifactorial neuropsychiatric syndrome comprising a broad range of cognitive and neurobehavioral symptoms. In critically ill patients, it may develop secondary to multiple predisposing factors. Although it can be transient and irreversible but if left untreated may lead to long term cognitive dysfunction. Early identification and assessment of risk factors usually help in appropriate management of delirium which in turn leads to decreased hospital stay, cost of therapy and mortality. Aim and Objective: Aim of the present study was to estimate the incidence of delirium using a validated scale in medical ICU patients and to determine the associated risk factors and outcomes. Material and Method: A prospective study in an 18-bed medical-intensive care unit (ICU) was undertaken. A total of 357 consecutive patients admitted to ICU for more than 24 hours were assessed. These patients were screened with the help of Confusion Assessment Method for Intensive Care Unit -CAM-ICU, Richmond Agitation and Sedation Scale, Screening Checklist for delirium and APACHE II. Appropiate statistical analysis was done to evaluate the risk factors influencing mortality in delirium. Results: Delirium occurred in 54.6% of 194 patients. Risk of delirium was independently associated with a history of hypertension, diabetes but not with severity of illness APACHE II score. Delirium was linked to longer ICU stay 13.08 ± 9.6 ver 7.07 ± 4.98 days, higher ICU mortality (35.8% % vs. 17.0%). Conclusion: Our study concluded that delirium poses a great risk factor in the outcome of the patient and carries high mortality, so a timely intervention helps in addressing these issues.

Keywords: delirium, risk factors, outcome, intervention

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Authors: Mehdi Mirzaei Alavijeh, Farzad Jalilian

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Background: Marijuana is the most widely used illicit drug worldwide, especially among adolescents and young adults, which can cause numerous complications. The aim of this study was to determine the pattern, motivation use, and factors related to marijuana use among Iranian youths based on the integrative model of behavioral prediction Methods: A cross-sectional study was conducted among 174 youths marijuana user in Kermanshah County and Isfahan County, during summer 2014 which was selected with the convenience sampling for participation in this study. A self-reporting questionnaire was applied for collecting data. Data were analyzed by SPSS version 21 using bivariate correlations and linear regression statistical tests. Results: The mean marijuana use of respondents was 4.60 times at during week [95% CI: 4.06, 5.15]. Linear regression statistical showed, the structures of integrative model of behavioral prediction accounted for 36% of the variation in the outcome measure of the marijuana use at during week (R2 = 36% & P < 0.001); and among them attitude, marijuana refuse, and subjective norms were a stronger predictors. Conclusion: Comprehensive health education and prevention programs need to emphasize on cognitive factors that predict youth’s health-related behaviors. Based on our findings it seems, designing educational and behavioral intervention for reducing positive belief about marijuana, marijuana self-efficacy refuse promotion and reduce subjective norms encourage marijuana use has an effective potential to protect youths marijuana use.

Keywords: marijuana, youth, integrative model of behavioral prediction, Iran

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Authors: Lee P. Leon, Raymond Charles

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This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.

Keywords: aggregate angularity, asphalt concrete, permanent deformation, rutting prediction

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Authors: Ayesha Aihetasham, Najma Arshad, Sobia Khan

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Damage causes by subterranean termites are of major concern today. Termites majorly treated with pesticides resulted in several problems related to health and environment. For this reason, plant-derived natural products specifically essential oils have been evaluated in order to control termites. The aim of the present study was to investigate the antitermitic potential of six essential oils on Heterotermes indicola subterranean termite. No-choice bioassay was used to assess the termiticidal action of essential oils. Further, gut from each set of treated termite group was extracted and analyzed for reduction in number of protozoa and bacteria by protozoal count method using haemocytometer and viable bacterial plate count (dilution method) respectively. In no-choice bioassay it was found that Foeniculum vulgare oil causes high degree of mortality 90 % average mortality at 10 mg oil concentration (10mg/0.42g weight of filter paper). Least mortality appeared to be due to Citrus sinensis oil (43.33 % average mortality at 10 mg/0.42g). The highest activity verified to be of Foeniculum vulgare followed by Eruca sativa, Trigonella foenum-graecum, Peganum harmala, Syzygium cumini and Citrus sinensis. The essential oil which caused maximum reduction in number of protozoa was P. harmala followed by T. foenum-graecum and E. sativa. In case of bacterial count E. sativa oil indicated maximum decrease in bacterial number (6.4×10⁹ CFU/ml). It is concluded that F. vulgare, E. sativa and P. harmala essential oils are highly effective against H. indicola termite and its gut microflora.

Keywords: bacterial count, essential oils, Heterotermes indicola, protozoal count

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Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

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Authors: Zeeshan Muzammal, Usama Latif, Fouzia Younas, Syed Muhammad Hassan, Samia Razaq

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Unexpected rainfall has always been an issue in the lifetime of crops and brings destruction for the farmers who harvest them. Unfortunately, Pakistan is one of the countries in which untimely rain impacts badly on crops like wash out of seeds and pesticides etc. Pakistan’s GDP is related to agriculture, especially in rural areas farmers sometimes quit farming because leverage of huge loss to their crops. Through our surveys and research, we came to know that farmers in the rural areas of Pakistan need rain information to avoid damages to their crops from rain. We developed a prototype using ICTs to inform the farmers about rain one week in advance. Our proposed solution has two ways of informing the farmers. In first we send daily messages about weekly prediction and also designed a helpline where they can call us to ask about possibility of rain.

Keywords: ICTD, farmers, mobile based, Pakistan, rural areas, weather prediction

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Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan

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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.

Keywords: decision tree, heart failure, data mining, classification model

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Authors: Quang Minh Dinh, Jian Guang Qin, Sabine Dittmann, Dinh Dac Tran

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The age and population structure the dermal gopy Parapocryptes serperaster were studied using length distributions, otolith and von Bertalanffy model in the Mekong Delta over a whole year through monthly sampling. The sex ratio of P. serperaster was near 1:1, and von Bertalanffy growth parameters were L∞= 25.2 cm, K = 0.74 yr-1, and t0 = -0.22 yr-1. Fish size at first entry to fishery was 14.6 cm, and fishing mortality (1.57 yr-1) and natural mortality (1.51 yr-1) accounted for 51% and 49% of the total mortality (3.07 yr-1), respectively. Relative yield-per-recruit and biomass-per-recruit analyses revealed the levels of maximum exploitation yield (Emax = 0.83), maximum economic yield (E0.1 = 0.71) and the yield at 50% reduction of exploitation (E0.5 = 0.37). Otoliths from 164 female and 196 male gobies were readable, and the otolith morphometry data were used for age identification. The mean age estimated by reading otolith annual rings and by analysing length frequency distribution was consistent. This study shows that the otolith morphometry is a reliable method for aging this goby and possibly also applicable for other tropical gobies. The fishery analysis indicates that this goby stock has not been overexploited in the Mekong Delta.

Keywords: Parapcryptes serperaster, otolith, age, pulation structure, Vietnam

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Authors: Sokkhey Phauk, Takeo Okazaki

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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

Procedia PDF Downloads 89

Authors: Vignesh Ratnaraj, Jaewon Chang

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Background: Frailty is associated with poorer outcomes in cardiac surgery, but the heterogeneity in frailty assessment tools makes it difficult to ascertain its true impact in cardiac surgery. Slow gait speed is a simple, validated, and reliable marker of frailty. We performed a systematic review and meta-analysis to examine the effect of slow gait speed on postoperative cardiac surgical patients. Methods: PubMED, MEDLINE, and EMBASE databases were searched from January 2000 to August 2021 for studies comparing slow gait speed and “normal” gait speed. The primary outcome was in-hospital mortality. Secondary outcomes were composite mortality and major morbidity, AKI, stroke, deep sternal wound infection, prolonged ventilation, discharge to a healthcare facility, and ICU length of stay. Results: There were seven eligible studies with 36,697 patients. Slow gait speed was associated with an increased likelihood of in-hospital mortality (risk ratio [RR]: 2.32; 95% confidence interval [CI]: 1.87–2.87). Additionally, they were more likely to suffer from composite mortality and major morbidity (RR: 1.52; 95% CI: 1.38–1.66), AKI (RR: 2.81; 95% CI: 1.44–5.49), deep sternal wound infection (RR: 1.77; 95% CI: 1.59–1.98), prolonged ventilation >24 h (RR: 1.97; 95% CI: 1.48–2.63), reoperation (RR: 1.38; 95% CI: 1.05–1.82), institutional discharge (RR: 2.08; 95% CI: 1.61–2.69), and longer ICU length of stay (MD: 21.69; 95% CI: 17.32–26.05). Conclusion: Slow gait speed is associated with poorer outcomes in cardiac surgery. Frail patients are twofold more likely to die during hospital admission than non-frail counterparts and are at an increased risk of developing various perioperative complications.

Keywords: cardiac surgery, gait speed, recovery, frailty

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

Procedia PDF Downloads 435

Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

Procedia PDF Downloads 106

Authors: Pedro Gomes, Lia Ferreira, Sofia Garcia, Jaime Babulal, Luís Costa, Luís Castelo, José Muras, Isabel Gonçalves, Rui Carvalho

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Introduction: Foot ulcers and their complications are an important cause of morbidity and mortality in diabetes. Objectives: The present study aims to evaluate the outcomes in terms of need for hospitalization, amputation, healing time and mortality in patients with new-onset diabetic foot ulcers in subgroups stratified by etiology. Methods: A retrospective study based on clinical assessment of patients presenting with new ulcers to a multidisciplinary diabetic foot consult during 2012. Outcomes were determined until September 2014, from hospital registers. Baseline clinical examination was done to classify ulcers as neuropathic, ischemic or neuroischemic. Results: 487 patients with new diabetic foot ulcers were observed; 36%, 15% and 49% of patients had neuropathic, ischemic and neuroischemic ulcers, respectively. For analysis, patients were classified as having predominantly neuropathic (36%) or ischemic foot (64%). The mean age was significantly higher in the group with ischemic foot (70±12 vs 63±12 years; p <0.001), as well as the duration of diabetes (18±10 vs 16 ± 10years, p <0.05). A history of previous amputation was also significantly higher in this group (24.7% vs 15.6%, p <0.05). The evolution of ischemic ulcers was significantly worse, with a greater need for hospitalization (27.2% vs 18%, p <0.05), amputation (11.5% vs 3.6% p <0.05) mainly major amputation (3% vs. 0%; p <0.001) and higher mean healing time (151 days vs 89 days, p <0.05). The mortality rate at 18 months, was also significantly higher in the ischemic foot group (7.3% vs 1.8%, p <0.05). Conclusions: All types of diabetic foot ulcers are associated with high morbidity and mortality, however, the presence of arterial disease confers a poor prognosis. Diabetic foot can be successfully treated only by the multidisciplinary team which can provide more comprehensive and integrated care.

Keywords: diabetes, foot ulcers, etiology, outcome

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Authors: K. Roushanger, A. Soleymanzadeh

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Weirs are structures often used in irrigation techniques, sewer networks and flood protection. However, the hydraulic behavior of this type of weir is complex and difficult to predict accurately. An accurate flow prediction over a weir mainly depends on the proper estimation of discharge coefficient. In this study, the Genetic Expression Programming (GEP) approach was used for predicting trapezoidal and rectangular sharp-crested side weirs discharge coefficient. Three different performance indexes are used as comparing criteria for the evaluation of the model’s performances. The obtained results approved capability of GEP in prediction of trapezoidal and rectangular side weirs discharge coefficient. The results also revealed the influence of downstream Froude number for trapezoidal weir and upstream Froude number for rectangular weir in prediction of the discharge coefficient for both of side weirs.

Keywords: discharge coefficient, genetic expression programming, trapezoidal weir

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Authors: Elham Sharifineyestani, Mohammad Farshchin

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Tropical cyclones are one of the most expensive and deadliest natural disasters. They cause heavy rainfall and serious flash flooding that result in billions of dollars of damage and considerable mortality each year in the United States. Prediction of the frequency of tropical cyclone-induced rainfall events can be helpful in emergency planning and flood risk management. In this study, we have developed a machine-learning model to predict the exceedance frequencies of tropical cyclone-induced rainfall events in the United States. Model results show a satisfactory agreement with available observations. To examine the effectiveness of our approach, we also have compared the result of our predictions with the exceedance frequencies predicted using a physics-based rainfall model by Feldmann.

Keywords: flash flooding, tropical cyclones, frequencies, machine learning, risk management

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Authors: Ryan M. Monti, Bijal Mehta

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In cases of massive pulmonary embolism, defined as acute pulmonary embolism presenting with systemic hypotension or right ventricular dysfunction and impending failure, there is indication that unconventional therapies, such as phosphodiesterase inhibitors, endothelin receptor antagonists, and/or prostacyclin analogs may decrease the morbidity and mortality. Based on the premise that dilating the pulmonary artery will decrease the pulmonary vascular pressure, while simultaneously decreasing the aggregation of platelets, it can be hypothesized that increased blood flow through the pulmonary artery will decrease right heart strain and subsequent morbidity and mortality. While this theory has yet to be formally studied, the recommendations for treating massive pulmonary embolism with phosphodiesterase inhibitors, endothelin receptor antagonists, and/or prostacyclin analogs in conjunction with the current standards of care in massive pulmonary embolism should be formally studied. In particular, patients with massive PE who are unable to undergo thrombolysis/surgical intervention may be the ideal population to study the use of these treatments to determine any decrease in mortality and morbidity (short term and long term).

Keywords: acute pulmonary thromboembolism, treatment of pulmonary embolism, use of phosphodiesterase inhibitors, endothelin receptor antagonists, prostacyclin analogs in PE

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Authors: Sheraz Ahmad Choudhary

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Health plays a vital role in the growth. The study examined the effect of health indicator on the growth of Pakistan. ARDL model is used to check the growth rate which is affected by the health by using the time series date of Pakistan from 1990 to 2017. Health indicator, fertility rate, life expectancy, foreign direct investment, and infant mortality rate are variables Where the unit root is applied to check the stationarity of the model. consequences find a significant relationship between GDP, foreign direct investment, fertility rate, and life expectancy in the short run, whereas mortality rate effected negatively to economic growth but have significant values. In the long run, foreign direct investment (FDI) and fertility rate(FR) have significantly influenced the GDP. The results show thateconomic growth is positively stimulated by most of the health indicators. The study accomplishes that nations can achieve a high level of economic growth by increasing wellbeing human capital.

Keywords: economic growth, health expenditures, fertility rate, human capital, life expectancy, foreign direct investment, and infant mortality rate

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Authors: Zenaida Chitu, Roxana Bojariu, Liliana Velea, Roxana Burcea

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A number of studies show that extreme air temperature affects mortality related to cardiovascular diseases, particularly among elderly people. In Romania, the summer thermal discomfort expressed by Universal Thermal Climate Index (UTCI) is highest in the Southern part of the country, where Bucharest, the largest Romanian urban agglomeration, is also located. The urban characteristics such as high building density and reduced green areas enhance the increase of the air temperature during summer. In Bucharest, as in many other large cities, the effect of heat urban island is present and determines an increase of air temperature compared to surrounding areas. This increase is particularly important during heat wave periods in summer. In this context, the researchers performed a temperature-mortality analysis based on daily deaths related to cardiovascular diseases, recorded between 2010 and 2019 in Bucharest. The temperature-mortality relationship was modeled by applying distributed lag non-linear model (DLNM) that includes a bi-dimensional cross-basis function and flexible natural cubic spline functions with three internal knots in the 10th, 75th and 90th percentiles of the temperature distribution, for modelling both exposure-response and lagged-response dimensions. Firstly, this study applied this analysis for the present climate. Extrapolation of the exposure-response associations beyond the observed data allowed us to estimate future effects on mortality due to temperature changes under climate change scenarios and specific assumptions. We used future projections of air temperature from five numerical experiments with regional climate models included in the EURO-CORDEX initiative under the relatively moderate (RCP 4.5) and pessimistic (RCP 8.5) concentration scenarios. The results of this analysis show for RCP 8.5 an ensemble-averaged increase with 6.1% of heat-attributable mortality fraction in future in comparison with present climate (2090-2100 vs. 2010-219), corresponding to an increase of 640 deaths/year, while mortality fraction due to the cold conditions will be reduced by 2.76%, corresponding to a decrease by 288 deaths/year. When mortality data is stratified according to the age, the ensemble-averaged increase of heat-attributable mortality fraction for elderly people (> 75 years) in the future is even higher (6.5 %). These findings reveal the necessity to carefully plan urban development in Bucharest to face the public health challenges raised by the climate change. Paper Details: This work is financed by the project URCLIM which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by Ministry of Environment, Romania with co-funding by the European Union (Grant 690462). A part of this work performed by one of the authors has received funding from the European Union’s Horizon 2020 research and innovation programme from the project EXHAUSTION under grant agreement No 820655.

Keywords: cardiovascular diseases, climate change, extreme air temperature, mortality

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Authors: Baeza S. Roberto

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The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

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Authors: Hafiza Javaria Ashraf, Asim Abbasi, Muhammad Hussnain Babar, Muhammad Sufyan

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Brevicoryne brassicae (Aphid) is not only the major biotic constraint of rapeseed crop but also transmits 20 different viral pathogens that cause diseases in crucifers. Aphids cause major losses to rapeseed by stunting growth and yield, with real damage being contamination of harvested heads. The misuse of pesticides has led to tremendous economic losses and hazards to human health and environmental pollution. Thus, newer approaches for pest control are continuously being sought. The naturally occurring, biologically active plant-based products seem to have a prominent role in the development of future commercial pesticides not only for increased productivity but their eco-friendly nature. The present experiment was carried out in Research Area of Ayub Agriculture Research Institute, Faisalabad to check the efficacy of different botanicals against rapeseed aphid. The tested botanicals were, neem seed extract, neem leaf extract, dathora seed extract, kaner leaf extract and aak leaf extract. Insecticide, advantage 20 EC served as the positive control in the experiment. Data was recorded before and after 1, 3 and 7 days of treatment application. The results of the experiment revealed that neem seed extract exhibited maximum mortality (48.42%) followed by dathora (45.54%) and kaner leaf extract (40.29%) after 7 days of treatment application. However minimum mortality i.e. 26.64% was observed in case of aak leaf extract. Advantage encountered maximum mortality i.e. 86.14%. All treatments caused maximum mortality after 7 days of treatment application. In case of pollinators maximum population reduction was observed in case of insecticide (74.29%) while minimum reduction was observed in neem leaf extract (11.57%). Hence it was concluded that unlike insecticides, plant based products can be a better option for regulating pests and conserving beneficial insect fauna.

Keywords: Aphid, mortality, plant based, pollinators

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Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

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Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

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Authors: Danilo López, Edwin Rivas, Fernando Pedraza

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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

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Authors: Kennedy Efosa Ehimwenma, Sujatha Krishnamoorthy, Safiya Al‑Sharji

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The probability computation of events is in the interval of [0, 1], which are values that are determined by the number of outcomes of events in a sample space S. The probability Pr(A) that an event A will never occur is 0. The probability Pr(B) that event B will certainly occur is 1. This makes both events A and B a certainty. Furthermore, the sum of probabilities Pr(E₁) + Pr(E₂) + … + Pr(Eₙ) of a finite set of events in a given sample space S equals 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. This paper first discusses Bayes, the complement of probability, and the difference of probability for occurrences of learning-events before applying them in the prediction of learning objects in student learning. Given the sum of 1; to make a recommendation for student learning, this paper proposes that the difference of argMaxPr(S) and the probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates i) the probability of skill-set events that have occurred that would lead to higher-level learning; ii) the probability of the events that have not occurred that requires subject-matter relearning; iii) accuracy of the decision tree in the prediction of student performance into class labels and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning.

Keywords: complement of probability, Bayes’ rule, prediction, pre-assessments, computational education, information theory

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Authors: Ahmad Rabby

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The present study emphasizing the catch trends assessment of Gray eel catfish (Plotosus canius) that was scrutinized on the basis of monthly length frequency data collected from mangrove estuary, Bangladesh during January 2017 to December 2018. A total amount of 1298 specimens were collected to estimate the total length (TL) and weight (W) of P. canius ranged from 13.3 cm to 87.4 cm and 28 g to 5200 g, respectively. The length-weight relationship was W=0.006 L2.95 with R2=0.972 for both sexes. The von Bertalanffy growth function parameters were L∞=93.25 cm and K=0.28 yr-1, hypothetical age at zero length of t0=0.059 years and goodness of the fit of Rn=0.494. The growth performances indices for L∞ and W∞ were computed as Φ'=3.386 and Φ=1.84, respectively. The size at first sexual maturity was estimated in TL as 48.8 cm for pool sexes. The natural mortality was 0.51 yr-1 at average annual water surface temperature as 22 0C. The total instantaneous mortality was 1.24 yr-1 at CI95% of 0.105–1.42 (r2=0.986). While fishing mortality was 0.73 yr-1 and the current exploitation ratio as 0.59. The recruitment was continued throughout the year with one major peak during May-June was 17.20-17.96%. The Beverton-Holt yield per recruit model was analyzed by FiSAT-II, when tc was at 1.43 yr, the Fmax was estimated as 0.6 yr-1 and F0.1 was 0.33 yr-1. Current age at the first capture was approximately 0.6 year, however Fcurrent = 0.73 yr-1 which is beyond the F0.1 indicated that the current stock of P. canius of Bangladesh was overexploited.

Keywords: Plotosus canius, mangrove estuary, asymptotic length, FiSAT-II

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Authors: Nicholas Bayfield, Liam Bibo, Charley Budgeon, Robert Larbalestier, Tom Briffa

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Introduction: Stress hyperglycaemia is common following cardiac surgery. Its optimal management is uncertain and may differ by diabetic status. This study assesses the in-hospital glycaemic management of cardiac surgery patients and associated postoperative outcomes. Methods: A retrospective cohort analysis of all patients undergoing cardiac surgery at Fiona Stanley Hospital from February 2015 to May 2019 was undertaken. Management and outcomes of hyperglycaemia following cardiac surgery were assessed. Follow-up was assessed to 1 year postoperatively. Multivariate regression modelling was utilised. Results: 1050 non-diabetic patients and 689 diabetic patients were included. In the non-diabetic cohort, patients with mild (peak blood sugar level [BSL] < 14.3), transient stress hyperglycaemia managed without insulin were not at an increased risk of wound-related morbidity (P=0.899) or mortality at 1 year (P=0.483). Insulin management was associated with wound-related readmission to hospital (P=0.004) and superficial sternal wound infection (P=0.047). Prolonged or severe stress hyperglycaemia was predictive of hospital re-admission (P=0.050) but not morbidity or mortality (P=0.546). Diabetes mellitus was an independent risk factor 1-year mortality (OR; 1.972 [1.041–3.736], P=0.037), graft harvest site wound infection (OR; 1.810 [1.134–2.889], P=0.013) and wound-related readmission (OR; 1.866 [1.076–3.236], P=0.026). In diabetics, postoperative peak BSL > 13.9mmol/L was predictive of graft harvest site infections (OR; 3.528 [1.724-7.217], P=0.001) and wound-related readmission OR; 3.462 [1.540-7.783], P=0.003) regardless of modality of management. A peak BSL of 10.0-13.9 did not increase the risk of morbidity/mortality compared to a peak BSL of < 10.0 (P=0.557). Diabetics with a peak BSL of 13.9 or less did not have significantly increased morbidity/mortality outcomes compared to non-diabetics (P=0.418). Conclusion: In non-diabetic patients, transient mild stress hyperglycaemia following cardiac surgery does not uniformly require treatment. In diabetic patients, postoperative hyperglycaemia with peak BSL exceeding 13.9mmol/L was associated with wound-related morbidity and hospital readmission following cardiac surgery.

Keywords: cardiac surgery, pulmonary embolism, pulmonary embolectomy, cardiopulmonary bypass

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Authors: Emmanuel Olamijuwon

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Despite laws against child marriage - a violation against child rights, the practice remains widespread in sub-Saharan Africa and globally partly because of persistent poverty, gender inequality, protection and the need to reinforce family ties. Using pooled data from the recent demographic and health surveys of 20-sub-Saharan African countries with a regional representative sample of 36,943 girls under 18 years, this study explores the prevalence, pattern and infant mortality implications of this marriage type while also examining its regional variations. Indications from the study are that child marriage is still very high in the region with variations above one-tenth in West, Central and Southern Africa regions except in the East African region where only about 7% of children under 18 were already married. Preliminary findings also suggest that about one-in-ten infant deaths were to child brides many of whom were residing in poor households, rural residence, unemployed and have less than secondary education. Based on these findings, it is, therefore, important that government of African countries addresses critical issues through increased policies towards increasing enrollment of girl children in schools as many of these girls are not likely to have any economic benefit to the region if the observed pattern continues.

Keywords: child marriage, infant mortality, Africa, child brides

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Authors: A. Bayaga

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This research provides a technical account of estimating Transition Probability using Time-homogeneous Markov Jump Process applying by South African HIV/AIDS data from the Statistics South Africa. It employs Maximum Likelihood Estimator (MLE) model to explore the possible influence of Transition Probability of mortality cases in which case the data was based on actual Statistics South Africa. This was conducted via an integrated demographic and epidemiological model of South African HIV/AIDS epidemic. The model was fitted to age-specific HIV prevalence data and recorded death data using MLE model. Though the previous model results suggest HIV in South Africa has declined and AIDS mortality rates have declined since 2002 – 2013, in contrast, our results differ evidently with the generally accepted HIV models (Spectrum/EPP and ASSA2008) in South Africa. However, there is the need for supplementary research to be conducted to enhance the demographic parameters in the model and as well apply it to each of the nine (9) provinces of South Africa.

Keywords: AIDS mortality rates, epidemiological model, time-homogeneous markov jump process, transition probability, statistics South Africa

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